@article {19279,
	title = {Analysis of SystemC actor networks for efficient synthesis},
	journal = {ACM Trans. Embed. Comput. Syst.},
	volume = {10},
	year = {2011},
	month = {2011},
	pages = {18:1 - 18:34},
	abstract = {Applications in the signal processing domain are often modeled by dataflow graphs. Due to heterogeneous complexity requirements, these graphs contain both dynamic and static dataflow actors. In previous work, we presented a generalized clustering approach for these heterogeneous dataflow graphs in the presence of unbounded buffers. This clustering approach allows the application of static scheduling methodologies for static parts of an application during embedded software generation for multiprocessor systems. It systematically exploits the predictability and efficiency of the static dataflow model to obtain latency and throughput improvements. In this article, we present a generalization of this clustering technique to dataflow graphs with bounded buffers, therefore enabling synthesis for embedded systems without dynamic memory allocation. Furthermore, a case study is given to demonstrate the performance benefits of the approach.},
	keywords = {actor-oriented design, clustering, Dataflow analysis, scheduling},
	isbn = {1539-9087},
	url = {http://doi.acm.org/10.1145/1880050.1880054},
	author = {Falk, Joachim and Zebelein, Christian and Keinert, Joachim and Haubelt, Christian and Teich, Juergen and Bhattacharyya, Shuvra S.}
}
@conference {17181,
	title = {Group-in-a-Box Layout for Multi-faceted Analysis of Communities},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {354 - 361},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a meta-layout for clustered graphs that enables multi-faceted analysis of networks. It uses the tree map space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets.},
	keywords = {Algorithm design and analysis, category based social graph partitions, clustered graphs, clustering, Clustering algorithms, Communities, data visualisation, force-directed, gender, geographic location, graph layout algorithms, graph theory, group-in-a-box, group-in-a-box layout, Image edge detection, Layout, meta-layout, multifaceted community analysis, network subgraph visualization, network visualization, pattern clustering, profession, semantic substrates, Social network services, social networking (online), social networks, treemap space filling technique, Visualization},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.139},
	author = {Rodrigues,E.M. and Milic-Frayling,N. and Smith,M. and Shneiderman, Ben and Hansen,D.}
}
@article {15262,
	title = {Achieving anonymity via clustering},
	journal = {ACM Trans. Algorithms},
	volume = {6},
	year = {2010},
	month = {2010/07//},
	pages = {49:1{\textendash}49:19 - 49:1{\textendash}49:19},
	abstract = {Publishing data for analysis from a table containing personal records, while maintaining individual privacy, is a problem of increasing importance today. The traditional approach of deidentifying records is to remove identifying fields such as social security number, name, etc. However, recent research has shown that a large fraction of the U.S. population can be identified using nonkey attributes (called quasi-identifiers) such as date of birth, gender, and zip code. The k-anonymity model protects privacy via requiring that nonkey attributes that leak information are suppressed or generalized so that, for every record in the modified table, there are at least k-1 other records having exactly the same values for quasi-identifiers. We propose a new method for anonymizing data records, where quasi-identifiers of data records are first clustered and then cluster centers are published. To ensure privacy of the data records, we impose the constraint that each cluster must contain no fewer than a prespecified number of data records. This technique is more general since we have a much larger choice for cluster centers than k-anonymity. In many cases, it lets us release a lot more information without compromising privacy. We also provide constant factor approximation algorithms to come up with such a clustering. This is the first set of algorithms for the anonymization problem where the performance is independent of the anonymity parameter k. We further observe that a few outlier points can significantly increase the cost of anonymization. Hence, we extend our algorithms to allow an ε fraction of points to remain unclustered, that is, deleted from the anonymized publication. Thus, by not releasing a small fraction of the database records, we can ensure that the data published for analysis has less distortion and hence is more useful. Our approximation algorithms for new clustering objectives are of independent interest and could be applicable in other clustering scenarios as well.},
	keywords = {anonymity, Approximation algorithms, clustering, privacy},
	isbn = {1549-6325},
	doi = {10.1145/1798596.1798602},
	url = {http://doi.acm.org/10.1145/1798596.1798602},
	author = {Aggarwal,Gagan and Panigrahy,Rina and Feder,Tom{\'a}s and Thomas,Dilys and Kenthapadi,Krishnaram and Khuller, Samir and Zhu,An}
}
@article {15566,
	title = {Approximation algorithm for the kinetic robust K-center problem},
	journal = {Computational Geometry},
	volume = {43},
	year = {2010},
	month = {2010/08//},
	pages = {572 - 586},
	abstract = {Two complications frequently arise in real-world applications, motion and the contamination of data by outliers. We consider a fundamental clustering problem, the k-center problem, within the context of these two issues. We are given a finite point set S of size n and an integer k. In the standard k-center problem, the objective is to compute a set of k center points to minimize the maximum distance from any point of S to its closest center, or equivalently, the smallest radius such that S can be covered by k disks of this radius. In the discrete k-center problem the disk centers are drawn from the points of S, and in the absolute k-center problem the disk centers are unrestricted.We generalize this problem in two ways. First, we assume that points are in continuous motion, and the objective is to maintain a solution over time. Second, we assume that some given robustness parameter 0 \&lt; t ⩽ 1 is given, and the objective is to compute the smallest radius such that there exist k disks of this radius that cover at least ⌈ t n ⌉ points of S. We present a kinetic data structure (in the KDS framework) that maintains a ( 3 + ε ) -approximation for the robust discrete k-center problem and a ( 4 + ε ) -approximation for the robust absolute k-center problem, both under the assumption that k is a constant. We also improve on a previous 8-approximation for the non-robust discrete kinetic k-center problem, for arbitrary k, and show that our data structure achieves a ( 4 + ε ) -approximation. All these results hold in any metric space of constant doubling dimension, which includes Euclidean space of constant dimension.
},
	keywords = {Approximation algorithms, clustering, kinetic data structures, robust statistics},
	isbn = {0925-7721},
	doi = {10.1016/j.comgeo.2010.01.001},
	url = {http://www.sciencedirect.com/science/article/pii/S0925772110000027},
	author = {Friedler,Sorelle A. and Mount, Dave}
}
@conference {13564,
	title = {Handwritten Arabic text line segmentation using affinity propagation},
	booktitle = {Proceedings of the 9th IAPR International Workshop on Document Analysis Systems},
	series = {DAS {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {135 - 142},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {In this paper, we present a novel graph-based method for extracting handwritten text lines in monochromatic Arabic document images. Our approach consists of two steps - Coarse text line estimation using primary components which define the line and assignment of diacritic components which are more difficult to associate with a given line. We first estimate local orientation at each primary component to build a sparse similarity graph. We then, use a shortest path algorithm to compute similarities between non-neighboring components. From this graph, we obtain coarse text lines using two estimates obtained from Affinity propagation and Breadth-first search. In the second step, we assign secondary components to each text line. The proposed method is very fast and robust to non-uniform skew and character size variations, normally present in handwritten text lines. We evaluate our method using a pixel-matching criteria, and report 96\% accuracy on a dataset of 125 Arabic document images. We also present a proximity analysis on datasets generated by artificially decreasing the spacings between text lines to demonstrate the robustness of our approach.},
	keywords = {affinity propagation, arabic, arabic documents, breadth-first search, clustering, dijkstra{\textquoteright}s shortest path algorithm, handwritten documents, line detection, text line segmentation},
	isbn = {978-1-60558-773-8},
	doi = {10.1145/1815330.1815348},
	url = {http://doi.acm.org/10.1145/1815330.1815348},
	author = {Kumar,Jayant and Abd-Almageed, Wael and Kang,Le and David Doermann}
}
@conference {16818,
	title = {Images in News},
	booktitle = {Pattern Recognition (ICPR), 2010 20th International Conference on},
	year = {2010},
	month = {2010/08//},
	pages = {3240 - 3243},
	abstract = {A system, called News Stand, is introduced that automatically extracts images from news articles. The system takes RSS feeds of news article and applies an online clustering algorithm so that articles belonging to the same news topic can be associated with the same cluster. Using the feature vector associated with the cluster, the images from news articles that form the cluster are extracted. First, the caption text associated with each of the images embedded in the news article is determined. This is done by analyzing the structure of the news article{\textquoteright}s HTML page. If the caption and feature vector of the cluster are found to contain keywords in common, then the image is added to an image repository. Additional meta-information are now associated with each image such as caption, cluster features, names of people in the news article, etc. A very large repository containing more than 983k images from 12 million news articles was built using this approach. This repository also contained more than 86.8 million keywords associated with the images. The key contribution of this work is that it combines clustering and natural language processing tasks to automatically create a large corpus of news images with good quality tags or meta-information so that interesting vision tasks can be performed on it.},
	keywords = {algorithm;feature, articles;news, clustering, clustering;publishing;, extraction;hypermedia, extraction;image, feeds;caption, HTML, images;news, LANGUAGE, languages;image, markup, page;RSS, processing;news, processing;pattern, repository;meta-information;natural, retrieval;natural, stand;online, text;feature, vector;image},
	doi = {10.1109/ICPR.2010.792},
	author = {Sankaranarayanan,J. and Samet, Hanan}
}
@conference {13086,
	title = {Web-scale computer vision using MapReduce for multimedia data mining},
	booktitle = {Proceedings of the Tenth International Workshop on Multimedia Data Mining},
	series = {MDMKDD {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {9:1{\textendash}9:10 - 9:1{\textendash}9:10},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {This work explores computer vision applications of the MapReduce framework that are relevant to the data mining community. An overview of MapReduce and common design patterns are provided for those with limited MapReduce background. We discuss both the high level theory and the low level implementation for several computer vision algorithms: classifier training, sliding windows, clustering, bag-of-features, background subtraction, and image registration. Experimental results for the k-means clustering and single Gaussian background subtraction algorithms are performed on a 410 node Hadoop cluster.},
	keywords = {background subtraction, bag-of-features, Cloud computing, clustering, Computer vision, image registration, MapReduce},
	isbn = {978-1-4503-0220-3},
	doi = {10.1145/1814245.1814254},
	url = {http://doi.acm.org/10.1145/1814245.1814254},
	author = {White,Brandyn and Tom Yeh and Jimmy Lin and Davis, Larry S.}
}
@conference {17911,
	title = {Salient Clustering for View-dependent Multiresolution Rendering},
	booktitle = {Computer Graphics and Image Processing (SIBGRAPI), 2009 XXII Brazilian Symposium on},
	year = {2009},
	month = {2009/10//},
	pages = {56 - 63},
	abstract = {Perceptual information is quickly gaining importance in mesh representation, analysis and rendering. User studies, eye tracking and other techniques are able to provide ever more useful insights for many user-centric systems, which form the bulk of computer graphics applications. In this work we build upon the concept of Mesh Saliency - an automatic measure of visual importance for triangle meshes based on models of low-level human visual attention - applying it to the problem of mesh segmentation and view-dependent rendering. We introduce a technique for segmentation that partitions an object into a set of face clusters, each encompassing a group of locally interesting features; Mesh Saliency is incorporated in a propagative mesh clustering framework, guiding cluster seed selection and triangle propagation costs and leading to a convergence of face clusters around perceptually important features. We compare our technique with different fully automatic segmentation algorithms, showing that it provides similar or better segmentation without the need for user input. We illustrate application of our clustering results through a saliency-guided view-dependent rendering system, achieving significant frame rate increases with little loss of visual detail.},
	keywords = {(computer, algorithms;cluster, analysis;mesh, attention;mesh, AUTOMATIC, centred, clustering, clustering;rendering, clustering;user-centric, clusters;low-level, dependent, design;, framework;salient, graphics);user, graphics;face, human, information;propagative, mesh, multiresolution, rendering;image, representation;mesh, resolution;image, saliency;mesh, seed, segmentation, segmentation;pattern, segmentation;perceptual, selection;computer, system;view, visual},
	doi = {10.1109/SIBGRAPI.2009.34},
	author = {Barni,R. and Comba,J. and Varshney, Amitabh}
}
@conference {15895,
	title = {Selecting hierarchical clustering cut points for web person-name disambiguation},
	booktitle = {Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval},
	series = {SIGIR {\textquoteright}09},
	year = {2009},
	month = {2009///},
	pages = {778 - 779},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Hierarchical clustering is often used to cluster person-names referring to the same entities. Since the correct number of clusters for a given person-name is not known a priori, some way of deciding where to cut the resulting dendrogram to balance risks of over- or under-clustering is needed. This paper reports on experiments in which outcome-specific and result-set measures are used to learn a global similarity threshold. Results on the Web People Search (WePS)-2 task indicate that approximately 85\% of the optimal F1 measure can be achieved on held-out data.},
	keywords = {clustering, person-name disambiguation},
	isbn = {978-1-60558-483-6},
	doi = {10.1145/1571941.1572124},
	url = {http://doi.acm.org/10.1145/1571941.1572124},
	author = {Gong,Jun and Oard, Douglas}
}
@article {13449,
	title = {LEARNING STRUCTURED BAYESIAN NETWORKS: COMBINING ABSTRACTION HIERARCHIES AND TREE-STRUCTURED CONDITIONAL PROBABILITY TABLES},
	journal = {Computational Intelligence},
	volume = {24},
	year = {2008},
	month = {2008/02/01/},
	pages = {1 - 22},
	abstract = {Context-specific independence representations, such as tree-structured conditional probability distributions, capture local independence relationships among the random variables in a Bayesian network (BN). Local independence relationships among the random variables can also be captured by using attribute-value hierarchies to find an appropriate abstraction level for the values used to describe the conditional probability distributions. Capturing this local structure is important because it reduces the number of parameters required to represent the distribution. This can lead to more robust parameter estimation and structure selection, more efficient inference algorithms, and more interpretable models. In this paper, we introduce Tree-Abstraction-Based Search (TABS), an approach for learning a data distribution by inducing the graph structure and parameters of a BN from training data. TABS combines tree structure and attribute-value hierarchies to compactly represent conditional probability tables. To construct the attribute-value hierarchies, we investigate two data-driven techniques: a global clustering method, which uses all of the training data to build the attribute-value hierarchies, and can be performed as a preprocessing step; and a local clustering method, which uses only the local network structure to learn attribute-value hierarchies. We present empirical results for three real-world domains, finding that (1) combining tree structure and attribute-value hierarchies improves the accuracy of generalization, while providing a significant reduction in the number of parameters in the learned networks, and (2) data-derived hierarchies perform as well or better than expert-provided hierarchies.},
	keywords = {abstraction hierarchies, background knowledge, Bayesian networks, clustering, machine learning, MDL},
	isbn = {1467-8640},
	doi = {10.1111/j.1467-8640.2007.00320.x},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8640.2007.00320.x/abstract;jsessionid=1C2AC5C6C44B47B8A26E3D5FC3F8FE74.d01t04},
	author = {desJardins, Marie and Rathod,Priyang and Getoor, Lise}
}
@conference {16839,
	title = {NewsStand: a new view on news},
	booktitle = {Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems},
	series = {GIS {\textquoteright}08},
	year = {2008},
	month = {2008///},
	pages = {18:1{\textendash}18:10 - 18:1{\textendash}18:10},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {News articles contain a wealth of implicit geographic content that if exposed to readers improves understanding of today{\textquoteright}s news. However, most articles are not explicitly geotagged with their geographic content, and few news aggregation systems expose this content to users. A new system named NewsStand is presented that collects, analyzes, and displays news stories in a map interface, thus leveraging on their implicit geographic content. NewsStand monitors RSS feeds from thousands of online news sources and retrieves articles within minutes of publication. It then extracts geographic content from articles using a custom-built geotagger, and groups articles into story clusters using a fast online clustering algorithm. By panning and zooming in NewsStand{\textquoteright}s map interface, users can retrieve stories based on both topical significance and geographic region, and see substantially different stories depending on position and zoom level.},
	keywords = {clustering, geotagging, knowledge discovery, text mining},
	isbn = {978-1-60558-323-5},
	doi = {10.1145/1463434.1463458},
	url = {http://doi.acm.org/10.1145/1463434.1463458},
	author = {Teitler,Benjamin E. and Lieberman,Michael D. and Panozzo,Daniele and Sankaranarayanan,Jagan and Samet, Hanan and Sperling,Jon}
}
@article {12241,
	title = {A scalable key management and clustering scheme for wireless ad hoc and sensor networks},
	journal = {Future Generation Computer Systems},
	volume = {24},
	year = {2008},
	month = {2008/10//},
	pages = {860 - 869},
	abstract = {This paper describes a scalable key management and clustering scheme for secure group communications in ad hoc and sensor networks. The scalability problem is solved by partitioning the communicating devices into subgroups, with a leader in each subgroup, and further organizing the subgroups into hierarchies. Each level of the hierarchy is called a tier or layer. Key generation, distribution, and actual data transmissions follow the hierarchy. The distributed, efficient clustering approach (DECA) provides robust clustering to form subgroups, and analytical and simulation results demonstrate that DECA is energy-efficient and resilient against node mobility. Comparing with most other schemes, our approach is extremely scalable and efficient, provides more security guarantees, and is selective, adaptive and robust.},
	keywords = {ad hoc networks, clustering, Group communications, Key management, sensor networks},
	isbn = {0167-739X},
	doi = {10.1016/j.future.2008.03.007},
	url = {http://www.sciencedirect.com/science/article/pii/S0167739X08000782},
	author = {Li,Jason H. and Bhattacharjee, Bobby and Yu,Miao and Levy,Renato}
}
@conference {18570,
	title = {Filtering spam with behavioral blacklisting},
	booktitle = {Proceedings of the 14th ACM conference on Computer and communications security},
	series = {CCS {\textquoteright}07},
	year = {2007},
	month = {2007///},
	pages = {342 - 351},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Spam filters often use the reputation of an IP address (or IP address range) to classify email senders. This approach worked well when most spam originated from senders with fixed IP addresses, but spam today is also sent from IP addresses for which blacklist maintainers have outdated or inaccurate information (or no information at all). Spam campaigns also involve many senders, reducing the amount of spam any particular IP address sends to a single domain; this method allows spammers to stay "under the radar". The dynamism of any particular IP address begs for blacklisting techniques that automatically adapt as the senders of spam change. This paper presents SpamTracker, a spam filtering system that uses a new technique called behavioral blacklisting to classify email senders based on their sending behavior rather than their identity. Spammers cannot evade SpamTracker merely by using "fresh" IP addresses because blacklisting decisions are based on sending patterns, which tend to remain more invariant. SpamTracker uses fast clustering algorithms that react quickly to changes in sending patterns. We evaluate SpamTracker{\textquoteright}s ability to classify spammers using email logs for over 115 email domains; we find that SpamTracker can correctly classify many spammers missed by current filtering techniques. Although our current datasets prevent us from confirming SpamTracker{\textquoteright}s ability to completely distinguish spammers from legitimate senders, our evaluation shows that SpamTracker can identify a significant fraction of spammers that current IP-based blacklists miss. SpamTracker{\textquoteright}s ability to identify spammers before existing blacklists suggests that it can be used in conjunction with existing techniques (e.g., as an input to greylisting). SpamTracker is inherently distributed and can be easily replicated; incorporating it into existing email filtering infrastructures requires only small modifications to mail server configurations.},
	keywords = {blacklists, botnets, clustering, Security, spam},
	isbn = {978-1-59593-703-2},
	doi = {10.1145/1315245.1315288},
	url = {http://doi.acm.org/10.1145/1315245.1315288},
	author = {Ramachandran,Anirudh and Feamster, Nick and Vempala,Santosh}
}
@conference {13443,
	title = {Interactive visual clustering},
	booktitle = {Proceedings of the 12th international conference on Intelligent user interfaces},
	series = {IUI {\textquoteright}07},
	year = {2007},
	month = {2007///},
	pages = {361 - 364},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Interactive Visual Clustering (IVC) is a novel method that allows a user to explore relational data sets interactively, in order to produce a clustering that satisfies their objectives. IVC combines spring-embedded graph layout with user interaction and constrained clustering. Experimental results on several synthetic and real-world data sets show that IVC yields better clustering performance than alternative methods.},
	keywords = {clustering, constraints, interaction},
	isbn = {1-59593-481-2},
	doi = {10.1145/1216295.1216367},
	url = {http://doi.acm.org/10.1145/1216295.1216367},
	author = {desJardins, Marie and MacGlashan,James and Ferraioli,Julia}
}
@article {15790,
	title = {QCS: A system for querying, clustering and summarizing documents},
	journal = {Information Processing \& Management},
	volume = {43},
	year = {2007},
	month = {2007/11//},
	pages = {1588 - 1605},
	abstract = {Information retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave across multiple systems. We present a novel integrated information retrieval system{\textemdash}the Query, Cluster, Summarize (QCS) system{\textemdash}which is portable, modular, and permits experimentation with different instantiations of each of the constituent text analysis components. Most importantly, the combination of the three types of methods in the QCS design improves retrievals by providing users more focused information organized by topic.We demonstrate the improved performance by a series of experiments using standard test sets from the Document Understanding Conferences (DUC) as measured by the best known automatic metric for summarization system evaluation, ROUGE. Although the DUC data and evaluations were originally designed to test multidocument summarization, we developed a framework to extend it to the task of evaluation for each of the three components: query, clustering, and summarization. Under this framework, we then demonstrate that the QCS system (end-to-end) achieves performance as good as or better than the best summarization engines.
Given a query, QCS retrieves relevant documents, separates the retrieved documents into topic clusters, and creates a single summary for each cluster. In the current implementation, Latent Semantic Indexing is used for retrieval, generalized spherical k-means is used for the document clustering, and a method coupling sentence {\textquotedblleft}trimming{\textquotedblright} and a hidden Markov model, followed by a pivoted QR decomposition, is used to create a single extract summary for each cluster. The user interface is designed to provide access to detailed information in a compact and useful format.
Our system demonstrates the feasibility of assembling an effective IR system from existing software libraries, the usefulness of the modularity of the design, and the value of this particular combination of modules.
},
	keywords = {clustering, Information retrieval, latent semantic indexing, Sentence trimming, Summarization, TEXT PROCESSING},
	isbn = {0306-4573},
	doi = {10.1016/j.ipm.2007.01.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0306457307000246},
	author = {Dunlavy,Daniel M. and O{\textquoteright}Leary,Dianne P. and Conroy,John M. and Schlesinger,Judith D.}
}
@conference {15244,
	title = {Achieving anonymity via clustering},
	booktitle = {Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems},
	series = {PODS {\textquoteright}06},
	year = {2006},
	month = {2006///},
	pages = {153 - 162},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Publishing data for analysis from a table containing personal records, while maintaining individual privacy, is a problem of increasing importance today. The traditional approach of de-identifying records is to remove identifying fields such as social security number, name etc. However, recent research has shown that a large fraction of the US population can be identified using non-key attributes (called quasi-identifiers) such as date of birth, gender, and zip code [15]. Sweeney [16] proposed the k-anonymity model for privacy where non-key attributes that leak information are suppressed or generalized so that, for every record in the modified table, there are at least k-1 other records having exactly the same values for quasi-identifiers. We propose a new method for anonymizing data records, where quasi-identifiers of data records are first clustered and then cluster centers are published. To ensure privacy of the data records, we impose the constraint that each cluster must contain no fewer than a pre-specified number of data records. This technique is more general since we have a much larger choice for cluster centers than k-Anonymity. In many cases, it lets us release a lot more information without compromising privacy. We also provide constant-factor approximation algorithms to come up with such a clustering. This is the first set of algorithms for the anonymization problem where the performance is independent of the anonymity parameter k. We further observe that a few outlier points can significantly increase the cost of anonymization. Hence, we extend our algorithms to allow an ε fraction of points to remain unclustered, i.e., deleted from the anonymized publication. Thus, by not releasing a small fraction of the database records, we can ensure that the data published for analysis has less distortion and hence is more useful. Our approximation algorithms for new clustering objectives are of independent interest and could be applicable in other clustering scenarios as well.},
	keywords = {anonymity, Approximation algorithms, clustering, privacy},
	isbn = {1-59593-318-2},
	doi = {10.1145/1142351.1142374},
	url = {http://doi.acm.org/10.1145/1142351.1142374},
	author = {Aggarwal,Gagan and Feder,Tom{\'a}s and Kenthapadi,Krishnaram and Khuller, Samir and Panigrahy,Rina and Thomas,Dilys and Zhu,An}
}
@conference {14464,
	title = {Iterative record linkage for cleaning and integration},
	booktitle = {Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery},
	series = {DMKD {\textquoteright}04},
	year = {2004},
	month = {2004///},
	pages = {11 - 18},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Record linkage, the problem of determining when two records refer to the same entity, has applications for both data cleaning (deduplication) and for integrating data from multiple sources. Traditional approaches use a similarity measure that compares tuples{\textquoteright} attribute values; tuples with similarity scores above a certain threshold are declared to be matches. While this method can perform quite well in many domains, particularly domains where there is not a large amount of noise in the data, in some domains looking only at tuple values is not enough. By also examining the context of the tuple, i.e. the other tuples to which it is linked, we can come up with a more accurate linkage decision. But this additional accuracy comes at a price. In order to correctly find all duplicates, we may need to make multiple passes over the data; as linkages are discovered, they may in turn allow us to discover additional linkages. We present results that illustrate the power and feasibility of making use of join information when comparing records.},
	keywords = {clustering, deduplication, distance measure, record linkage},
	isbn = {1-58113-908-X},
	doi = {10.1145/1008694.1008697},
	url = {http://doi.acm.org/10.1145/1008694.1008697},
	author = {Bhattacharya,Indrajit and Getoor, Lise}
}
@article {17229,
	title = {In vivo filtering of in vitro expression data reveals MyoD targets},
	journal = {Comptes Rendus Biologies},
	volume = {326},
	year = {2003},
	month = {2003/10//},
	pages = {1049 - 1065},
	abstract = {A published set of downstream targets of MyoD defined in a well-controlled in vitro experiment was filtered for relevance to muscle regeneration using a 27-time-point in vivo murine regeneration series. Using interactive hierarchical and Bayes soft clustering, only a minority of the targets defined in vitro can be confirmed in vivo (\~{}50\% of induced transcripts, and none of repressed transcripts). This approach provided strong support that 18 targets including of MyoD are biologically relevant during myoblast differentiation. To cite this article: P.~Zhao et al., C.~R. Biologies 326 (2003).},
	keywords = {clustering, expression profiling, Microarray, micror{\'e}seaux, muscle regeneration, MyoD, profil d{\textquoteright}expression, r{\'e}g{\'e}n{\'e}ration musculaire, regroupement},
	isbn = {1631-0691},
	doi = {10.1016/j.crvi.2003.09.035},
	url = {http://www.sciencedirect.com/science/article/pii/S1631069103002324},
	author = {Zhao,Po and Seo,Jinwook and Wang,Zuyi and Wang,Yue and Shneiderman, Ben and Hoffman,Eric P}
}
@conference {13239,
	title = {Mean-shift analysis using quasiNewton methods},
	booktitle = {Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {II - 447-50 vol.3 - II - 447-50 vol.3},
	abstract = {Mean-shift analysis is a general nonparametric clustering technique based on density estimation for the analysis of complex feature spaces. The algorithm consists of a simple iterative procedure that shifts each of the feature points to the nearest stationary point along the gradient directions of the estimated density function. It has been successfully applied to many applications such as segmentation and tracking. However, despite its promising performance, there are applications for which the algorithm converges too slowly to be practical. We propose and implement an improved version of the mean-shift algorithm using quasiNewton methods to achieve higher convergence rates. Another benefit of our algorithm is its ability to achieve clustering even for very complex and irregular feature-space topography. Experimental results demonstrate the efficiency and effectiveness of our algorithm.},
	keywords = {analysis;, classification;, clustering, clustering;, Convergence, density, estimation;, feature-space, image, irregular, iterative, Mean-shift, method;, methods;, Newton, nonparametric, pattern, procedure;, quasiNewton, rates;, segmentation;, technique;, topography;},
	doi = {10.1109/ICIP.2003.1246713},
	author = {Yang,C. and Duraiswami, Ramani and DeMenthon,D. and Davis, Larry S.}
}
@conference {17575,
	title = {Clustering and server selection using passive monitoring},
	booktitle = {IEEE INFOCOM 2002. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {1717- 1725 vol.3 - 1717- 1725 vol.3},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We consider the problem of client assignment in a distributed system of content servers. We present a system called Webmapper for clustering IP addresses and assigning each cluster to an optimal content server. The system is passive in that the only information it uses comes from monitoring the TCP connections between the clients and the servers. It is also flexible in that it makes no a priori assumptions about network topology and server placement and it can react quickly to changing network conditions. We present experimental results to evaluate the performance of Webmapper.},
	keywords = {client assignment, client-server systems, clustering, content servers, Delay, distributed system, Educational institutions, Internet, IP addresses, Monitoring, network conditions, Network servers, Network topology, optimal content server, passive monitoring, server selection, Space technology, TCPIP, Transport protocols, Web pages, Web server, Webmapper},
	isbn = {0-7803-7476-2},
	doi = {10.1109/INFCOM.2002.1019425},
	author = {Andrews,M. and Shepherd,B. and Srinivasan, Aravind and Winkler,P. and Zane,F.}
}
@article {15613,
	title = {An efficient k-means clustering algorithm: analysis and implementation},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {24},
	year = {2002},
	month = {2002/07//},
	pages = {881 - 892},
	abstract = {In k-means clustering, we are given a set of n data points in d-dimensional space R<sup>d</sup> and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd{\textquoteright}s (1982) algorithm. We present a simple and efficient implementation of Lloyd{\textquoteright}s k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm{\textquoteright}s running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation},
	keywords = {algorithm;color, algorithm;image, algorithm;kd-tree;mean, analysis;filtering, clustering, clustering;, compression;data, distance;covariance, Lloyd, matrices;filtering, quantization;data, segmentation;k-means, squared, structure;data-sensitive, theory;pattern},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2002.1017616},
	author = {Kanungo,T. and Mount, Dave and Netanyahu,N. S and Piatko,C. D and Silverman,R. and Wu,A. Y}
}
@conference {15624,
	title = {A local search approximation algorithm for k-means clustering},
	booktitle = {Proceedings of the eighteenth annual symposium on Computational geometry},
	series = {SCG {\textquoteright}02},
	year = {2002},
	month = {2002///},
	pages = {10 - 18},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {In k-means clustering we are given a set of n data points in d-dimensional space Rd and an integer k, and the problem is to determine a set of k points in {\'O}C;d, called centers, to minimize the mean squared distance from each data point to its nearest center. No exact polynomial-time algorithms are known for this problem. Although asymptotically efficient approximation algorithms exist, these algorithms are not practical due to the extremely high constant factors involved. There are many heuristics that are used in practice, but we know of no bounds on their performance.We consider the question of whether there exists a simple and practical approximation algorithm for k-means clustering. We present a local improvement heuristic based on swapping centers in and out. We prove that this yields a (9+\&egr;)-approximation algorithm. We show that the approximation factor is almost tight, by giving an example for which the algorithm achieves an approximation factor of (9-\&egr;). To establish the practical value of the heuristic, we present an empirical study that shows that, when combined with Lloyd{\textquoteright}s algorithm, this heuristic performs quite well in practice.},
	keywords = {Approximation algorithms, clustering, computational geometry, k-means, local search},
	isbn = {1-58113-504-1},
	doi = {10.1145/513400.513402},
	url = {http://doi.acm.org/10.1145/513400.513402},
	author = {Kanungo,Tapas and Mount, Dave and Netanyahu,Nathan S. and Piatko,Christine D. and Silverman,Ruth and Wu,Angela Y.}
}
