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Topic: Data clustering

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	Different Techniques of Data Clustering
		Data clustering is a method in which we make cluster of objects that are somehow similar in characteristics.
		Each cluster may be represented by a centroid or a cluster representative; this is some sort of summary description of all the objects contained in a cluster.
		The applications of clustering are also discussed with the examples of medical images database, data mining using data clustering and finally the case study of windows NT.
	members.tripod.com /asim_saeed/paper.htm (3224 words)

	Data clustering - Wikipedia, the free encyclopedia
		Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics.
		Clustering is the classification of similar objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance measure.
		Formal concept analysis is a technique for generating clusters of objects and attributes, given a bipartite graph representing the relations between the objects and attributes.
	en.wikipedia.org /wiki/Data_clustering (2596 words)

	DMS Tutorial - Clustering techniques (Site not responding. Last check: 2007-10-15)
		clusters might have hierarchical structure, having crude division of examples at highest level of hierarchy, which is then refined to sub-clusters at lower levels.
		When dealing with clustering techniques, one has to adopt a notion of a high dimensional space, or space in which ortogonal dimensions are all attributes from the table of data we are analyzing.
		Most of the issues related to automatic cluster detection are connected to the kinds of questions we want to be answered in the data mining project, or data preparation for their successful application.
	dms.irb.hr /tutorial/tut_clustering_short.php (859 words)

	T-106.850 Seminar on String Algorithms 2003 - Data Clustering
		An example of such methods is the K-means clustering algorithm, in which K points in the space in which the data points are spread are chosen as cluster centers and each of the N data points it assigned to a particular center.
		Data points representing the different matrices could then be assigned coordinates depending on their structure and thereafter clustered based on proximity.
		Hence the clustering is a partition (or a cover) of U such that the distance between points in the same cluster is "as small as feasible" and the distance between points in different clusters is "as large as possible".
	www.tcs.hut.fi /~satu/mja (3835 words)

	Data Clustering
		Instead of a clustering, the output is a difference file of times each pair of items was placed in different clusters.
		Before clustering, all values are increased by a random value between zero and sd times the specified value, where sd is the standard deviation of all the original values.
		This cluster is indicated in the figure by the line connecting Norwegian to Swedish.
	odur.let.rug.nl /~kleiweg/clustering/clustering.html (1247 words)

	Computational Statistics: Clustering
		Clustering refers to several related problems: partitioning a set of input points into a fixed number of "closely related" subsets; finding a small number of representative center points; or matching the point distribution to a family of overlapping continuous distributions.
		It may be ok (or even desired) that a large cluster in the actual data be represented by several centers in the cluster output, but it should not be possible for a large cluster of data points to be missing a representative.
		The regression depth of a given hyperplane (measured as a fraction of the size of the overall data set) is within epsilon of the regression depth of the hyperplane relative to an epsilon-approximation of the data.
	www.ics.uci.edu /~eppstein/280/cluster.html (2480 words)

	Hub4 Language Modeling Using Domain Interpolation and Data Clustering
		For the purposes of the evaluation, the data in the Hub4 benchmark were partitioned into seven different focus conditions [7].
		A backoff trigram model was built for each cluster, and interpolated with a trigram model derived from all articles for smoothing, to compensate for the different amounts of training data per cluster.
		The interpolation weight for the cluster LM and the general LM was tuned by maximizing the likelihood of the segments in the story cluster corresponding to the cluster LM.
	www.nist.gov /speech/publications/darpa97/html/weng1/weng1.htm (3530 words)

	Amazon.fr : Clustering For Data Mining: A Data Recovery Approach: Livres en anglais: Boris Mirkin (Site not responding. Last check: 2007-10-15)
		Even the most popular clustering methods--K-Means for partitioning the data set and Ward's method for hierarchical clustering--have lacked the theoretical attention that would establish a firm relationship between the two methods and relevant interpretation aids.
		The author suggests original methods for both cluster finding and cluster description, addresses related topics such as principal component analysis, contingency measures, and data visualization, and includes nearly 60 computational examples covering all stages of clustering, from data pre-processing to cluster validation and results interpretation.
		This author's unique attention to data recovery methods, theory-based advice, pre- and post-processing issues that are beyond the scope of most texts, and clear, practical instructions for real-world data mining make this book ideally suited for virtually all purposes: for teaching, for self-study, and for professional reference.
	www.amazon.fr /Clustering-Data-Mining-Recovery-Approach/dp/1584885343 (478 words)

	Tutorial on DNA array data clustering (Site not responding. Last check: 2007-10-15)
		Data are arranged in a table where rows contain the different gene expression values for a given gene in the different experimental conditions.
		This is the hierarchical classification obtained for the previous data using SOTA and lineal correlation as distance.
		The variability is defined in each cluster as the largest among all the pattern-pattern distances in it.
	bioinfo.cnio.es /docus/courses/clustering_tutorial (1160 words)

	Clustering - Introduction (Site not responding. Last check: 2007-10-15)
		Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data.
		In the first case data are grouped in an exclusive way, so that if a certain datum belongs to a definite cluster then it could not be included in another cluster.
		If the components of the data instance vectors are all in the same physical units then it is possible that the simple Euclidean distance metric is sufficient to successfully group similar data instances.
	www.elet.polimi.it /upload/matteucc/Clustering/tutorial_html/index.html (1022 words)

	Survey Of Clustering Data Mining Techniques - Berkhin (ResearchIndex) (Site not responding. Last check: 2007-10-15)
		Data modeling puts clustering in a historial perspective rooted in mathematics, statistics and numerical analysis.
		28 Density-based clustering in spatial databases: the algorithm..
		13 Probabilistic model-based clustering of multivariate and seq..
	citeseer.ist.psu.edu /berkhin02survey.html (2432 words)

	Research on Clustering
		The goal of data clustering, or unsupervised learning, is to discover "natural" groupings in a set of patterns, points, or objects, without prior knowledge of any class labels.
		There are many applications of cluster analysis, including vector quantization, image segmentation, constructing the prototypes of classifiers, understanding genomic data, market segmentation, etc. Despite its long history, clustering still poses a number of open research problems.
		Unfortunately, clusters in real world data sets are "heterogeneous" (of diverse shapes and data densities), and it is difficult for a single clustering algorithm to detect different types of clusters.
	dataclustering.cse.msu.edu (975 words)

	CS369C: Clustering Algorithms
		Data types include categorical vs. numerical, static vs. dynamic, points in a metric space vs. vertices in a graph.
		Because of its recent ubiquitous applicability, the field of clustering has undergone major revolution over the last few decades characterized by advances in approximation and randomized algorithms, novel formulations of the clustering problem, algorithms for clustering massively large data sets, algorithms for clustering data streams, and dimension reduction techniques.
		Clustering Large Datasets in Arbitrary Metric Spaces, V. Ganti, R. Ramakrishnan, J. Gehrke, A. Powell, and J.C. French.
	theory.stanford.edu /~nmishra/cs369C-2005.html (1122 words)

	HCE - Hierarchical Clustering Explorer
		Other clustering algorithms automatically determine the right number of clusters, but users may not be convinced of the result since they had little or no control over the clustering process.
		To avoid this dilemma, the Hierarchical Clustering Explorer (HCE) applies the hierarchical clustering algorithm without a predetermined number of clusters, and then enables users to determine the natural grouping with interactive visual feedback (dendrogram and color mosaic) and dynamic query controls.
		In addition, it is not efficient to perform a cluster analysis over the whole data set in cases where researchers know the approximate temporal pattern of the gene expression that they are seeking.
	www.cs.umd.edu /hcil/multi-cluster (1243 words)

	Data Clustering Engine Exceeds Expectations for IBM Brazil
		The Data Clustering Engine is running on a RS/6000 53H with AIX 4.1.
		The Data Clustering Engine is intelligent software that matches and groups records using names, address and other identification data.
		Despite data quality, this software allows diverse data records to be grouped into "clusters" of persons, households, organizations or any relationship hidden in the data.
	www.dmreview.com /article_sub.cfm?articleId=866 (504 words)

	Data Clustering
		Note: clustering with side-information (particularly constraints) are listed in another page.
		Cluster Analysis Algorithms for Data Reduction and Classification of Objects.
		Clustering course by Nina Mishra at the theory group at Stanford.
	www.cse.msu.edu /~lawhiu/clustering/index.html (630 words)

	[No title]
		The quality of a clustering result also depends on both the similarity measure used by the method and its implementation.
		The quality of a clustering method is also measured by its ability to discover some or all of the hidden patterns.
		The challenges for the data mining community is to develop clustering methods that adequately address all these special requirements.
	master.cpe.ku.ac.th /mcpe/204562/cluster1.ppt (809 words)

	CLUTO - Family of Data Clustering Software Tools \| Karypis Lab
		CLUTO is a family of computationally efficient and high-quality data clustering and cluster analysis programs & libraries, that are well suited for low- and high-dimensional data sets.
		CLUTO is a software package for clustering low- and high-dimensional datasets and for analyzing the characteristics of the various clusters.
		CLUTO is well-suited for clustering data sets arising in many diverse application areas including information retrieval, customer purchasing transactions, web, GIS, science, and biology.
	glaros.dtc.umn.edu /gkhome/views/cluto (181 words)

	Data Clustering: A Review - Jain, Murty, Flynn (ResearchIndex)
		Abstract: This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
		We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances.
		We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information...
	citeseer.ist.psu.edu /jain99data.html (307 words)

	Amazon.com: Classification, Clustering and Data Analysis: Books: Krzystof Jajuga (Site not responding. Last check: 2007-10-15)
		In particular, these include: classification models and clustering methods, multivariate data analysis, symbolic data, neural networks and learning devices, phylogeny and bioinformatics, new software systems for classification and data analysis, as well as applications in social, economic, biological, medical and other sciences.
		Deals with recent developments in classification and data analysis and presents new topics which are of central interested to modern statistics.
		Data Analysis from Information Builders — Information Builders's Webfocus suite of OLAP and analytics tools allows deep analysis of your existing legacy data sources with or without a cube.
	www.amazon.com /Classification-Clustering-Analysis-Krzystof-Jajuga/dp/354043691X (1024 words)

	BioMedical Engineering OnLine \| Full text \| Review of "clustering for data mining: a data recovery approach" by Boris ...
		After reviewing this book, as a researcher in the field of Intelligent Data mining and Data Clustering I should say that this is a very good and easy reading book with very strong scientific and mathematical material.
		One can read through these chapters keeping the clustering problems in mind and then decide which approach would be best suitable for the particular problem under consideration.
		Overall, I should recommend all engineering students and research engineers to read this book, especially those who are doing research in the field of data clustering, classification algorithms development, intelligent signal processing, biomedical and sensory classification problems.
	www.biomedical-engineering-online.com /content/5/1/34 (567 words)

	Publications and Abstracts (Site not responding. Last check: 2007-10-15)
		Bernd Fischer and Joachim M. Buhmann, Data Resampling for Path Based Clustering, in: L. Van Gool (Editor), Pattern Recognition - Symposium of the DAGM 2002, pp.
		J. Buhmann and T. Hofmann, A Maximum Entropy Approach to Pairwise Data Clustering, in Proceedings of the International Conference on Pattern Recognition, Hebrew University, Jerusalem, vol.II, IEEE Computer Society Press, pp.207-212, 1994.
		Thomas Hofmann and Joachim M. Buhmann, Inferring Hierarchical Clustering Structures by Deterministic Annealing.In: Proceedings of the 2nd Int.
	www-dbv.informatik.uni-bonn.de /papers (1909 words)

	Open Directory - Computers: Software: Databases: Data Mining (Site not responding. Last check: 2007-10-15)
		Data Mining and Knowledge Discovery - A peer-reviewed journal publishing articles on all aspects of Knowledge Discovery in Databases (KDD) and data mining methods for extracting high-level representations (patterns and models) from data.
		Data can be sorted, filtered, printed and exported to a variety of formats.
		Bank Of Montreal Mines Knowledge From Data - Jan Mrazek says privacy and performance are key issues in business intelligence and data mining for the Bank of Montreal.
	dmoz.org /Computers/Software/Databases/Data_Mining (510 words)

	Parallel K-Means Data Clustering
		Note: The first K elements of the input data are picked as the initial K cluster center coordinates.
		Each line contains two integers: data point index (from 0 to the number of points) and the cluster id indicating the membership of the point.
		Data type -- This implementation uses C float data type for all coordinates and other real numbers.
	www.ece.northwestern.edu /~wkliao/Kmeans/index.html (330 words)

	Data Clustering :: Identity Systems
		Despite the error, variation and duplication inherent in large databases, our algorithms deliver the highest possible reliability and data clustering when searching, matching or grouping your data based on names, addresses, descriptions and other identification data.
		When you need data clustering done right the first time, know that Identity Systems software provides you high quality results with formatted or unformatted data, cleaned or in its raw state; with your organization's internal and external files, including databases with extreme volumes of data.
		Our Data Clustering Product is designed with the flexibility and strength to satisfy your varied requirements.
	www.identitysystems.com /data-clustering.html (202 words)

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