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	Sandia National Laboratories - Cluster analysis
		The pair successfully used cluster analysis, a technique for grouping similar entities in such a way that their interrelationships are revealed, to identify explosions from mines in New Mexico and Wyoming.
		Cluster analysis offers a way to narrow the field of events that must be monitored by eliminating explosions from known mines.
		Before performing the official cluster analyses, the Sandia researchers ran test sets preprocessing the data to bring out specific characteristics in the waveforms and using different hierarchical cluster analysis methods to determine which was the best for their purposes.
	www.sandia.gov /LabNews/LN09-24-99/cluster_story.html (864 words)

	Interpreting Cluster Analysis for Researchers
		Cluster analysis is a popular classification technique frequently used to analyze market research data which divides the data into groups.
		Cluster analysis is most often used in cases in which it is unknown, prior to the analysis, the number of groups in the data or which observations belong to which groups.
		Hierarchical methods, in which clusters are defined according to similarity or dissimilarity measures, remain the most popular method of analysis, and user friendly software makes the analysis easily accessible to a wide variety of researchers in a variety of fields.
	www.camo.com /rt/Resources/infodoc/Interpreting_Cluster_Analysis.html (1113 words)

	PA 765: Cluster Analysis
		The cluster to be merged is the one with the smallest sum of Euclidean distances between cluster means (centroids) for all variables.
		Relative importance of variables in two-step cluster analysis is shown in a chi-square plot in which the y axis is the set of variables and the x axis is the chi-square value.
		In cluster analysis the groups (clusters) are not predetermined and in fact the object is to determine the best way in which cases may be clustered into groups.
	www2.chass.ncsu.edu /garson/pa765/cluster.htm (5633 words)

	Internetworking (2.3): Article-Cluster Analysis
		Cluster analysis quantifies card-sorting data by calculating the strength of the perceived relationships between pairs of cards, based on how often the members of each possible pair appear in a common group.
		Cluster analysis programs can display output in the form of tree diagrams, in which the relationship between each pair of cards is represented graphically by the distance between the origin and the branching of the lines leading to the two cards (see Interpreting the diagrams below for an explanation of a sample card-sorting tree diagram).
		Cluster analysis is rarely applied to card-sorting data, probably due to the tedious procedures required for getting the user data into, and interpreting the output of, currently available statistical packages such as SAStm or Statisticatm.
	www.internettg.org /newsletter/dec99/cluster_analysis.html (2214 words)

	Cluster analysis - Sarle
		Since clustering methods attempt to maximize the separation between clusters, the assumptions of the usual significance tests, parametric or nonparametric, are drastically violated.
		If the estimated number of modal clusters is constant for a wide range of k values, there is strong evidence of at least that many modes in the population.
		Girman, C.J. (1994), "Cluster Analysis and Classification Tree Methodologyas an Aid to Improve Understandinh of Benign Prostatic Hyperplasia," Ph.D. thesis, Chapel Hill, NC: Department of Biostatistics, University of North Carolina.
	www.pitt.edu /~wpilib/clusfaq.html (3005 words)

	What Is Cluster Analysis
		Cluster analysis is an exploratory data analysis tool for solving classification problems.
		Each cluster thus describes, in terms of the data collected, the class to which its members belong; and this description may be abstracted through use from the particular to the general class or type.
		Cluster analysis might provide the methodology to help you solve it; and Clustan could provide the professional software you need for that task.
	www.clustan.com /what_is_cluster_analysis.html (150 words)

	Cluster Analysis
		In other words cluster analysis is an exploratory data analysis tool which aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise.
		The goal of the clustering algorithm then is to maximize the overall probability or likelihood of the data, given the (final) clusters.
		Cluster analysis is an unsupervised learning technique, and we cannot observe the (real) number of clusters in the data.
	www.statsoftinc.com /textbook/stcluan.html (3780 words)

	Cluster Analysis Description
		Hierarchical Cluster Analysis Methods: In general, hierarchical CA methods proceed by creating groups of observations through merging or dividing (Gong and Richman 1995) and are broken into two main categories; agglomerative and divisive.
		After determining the desired number of clusters in a dataset (either by subjective choice or the use of other cluster techniques to suggest such a number) this number is used to specify the number of "seed points" inserted into the domain.
		In addition to using agglomerative clustering methods to determine the appropriate number of clusters to retain, there are statistical methods of investigating different K means solutions to determine whether the choice of a particular number of clusters is appropriate.
	met.psu.edu /~arnottj/myweb/Cluster_Analysis_Description.html (2009 words)

	Clustering
		Cluster analysis identifies and classifies objects individuals or variables on the basis of the similarity of the characteristics they possess.
		A shortcoming of single-linkage clustering: Cluster A goes from upper left to lower right and might be fit by a bivariate normal distribution with negative correlation; Cluster B, from lower left to upper right, by one with positive correlation.
		Now continue clustering by complete linkage:-- Form a new matrix of squared distances, where the squared distance between clusters is the maximum of the squared distances, for all pairs formed with one member in one cluster and the other member in the other.
	www.uic.edu /classes/idsc/ids472/clustering.htm (1864 words)

	Making Sense of Clusters: Regional Competitiveness and Economic Development (Site not responding. Last check: )
		An industry cluster is a group of firms, and related economic actors and institutions, that are located near one another and that draw productive advantage from their mutual proximity and connections.
		The cluster approach leads to little if any reliance on economic development subsidies and recruitment efforts aimed at individual firms; if these individual, firm-based policies are used at all, they should be focused on firms that fit within existing clusters.
		Much of the research on clusters has been preoccupied with debating the precise definition of a cluster, applying a single methodology, or examining whether clusters are good or bad for various measures of regional economic success.
	www.brookings.edu /metro/mei/20060727_clusters.htm (786 words)

	ED231A: Cluster Analysis
		Cluster analysis is one of those techniques that is very attractive to both students and researchers alike.
		The idea behind cluster analysis is very simple, that is, to identify groupings or clusters of individuals, using multiple variables, that are not readily aparent to the researcher.
		Cluster analysis is a collection of techniques and algorithms which often classify the same observations into completely different groupings.
	www.gseis.ucla.edu /courses/ed231a1/notes2/cluster0.html (437 words)

	Paul E. Green, Frank J. Carmone Jr., and Scott M. Smith MULTIDIMENSIONAL SCALING Section 5 DIMENSION-REDUCING METHODS ... (Site not responding. Last check: )
		Cluster analysis is thus concerned ultimately with classification and represents a set of techniques which are part of the field of numerical taxonomy (Frank and Green, [1968]; Punj and Stewart [1983]; Aldenderfer and Blashfield [1984]).
		Clustering procedures can be viewed as "preclassificatory" in the sense that the researcher has not used prior judgment to partition the subjects (rows of the data matrix).
		In addition, clusters may be added, deleted, or modified to produce constrained solutions [Carroll and Arabie, 1980], and estimate (in a regression sense) the importance of new sets of clusters in explaining variance in the data.
	marketing.byu.edu /htmlpages/tutorials/cluster.htm (8192 words)

	Cluster Analysis
		In other words cluster analysis is an exploratory data analysis tool which aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise.
		In addition to identifying such clusters, it is usually equally of interest to determine how the clusters are different, i.e., determine the specific variables or dimensions that vary and how they vary in regard to members in different clusters.
		Cluster analysis is an unsupervised learning technique, and we cannot observe the (real) number of clusters in the data.
	www.statsoft.com /textbook/stcluan.html (3780 words)

	Cluster Analysis for Researchers by Charles Romesburg (Book) in Medicine & Science
		It is the best book on the subject of cluster analysis, which I use frequently in my ecological and satellite remote sensing areas of research.
		I teach this powerful methodology to my students in my field techniques course, and show students how cluster analysis is used to group spectral values derived from satellite remote sensing instruments orbiting the earth.
		Anyone who uses cluster analysis, or who might use cluster analysis should not be without this book.
	www.lulu.com /content/46479 (414 words)

	Welcome to ZHA, INC. : Economic Development
		As a result of the cluster analysis, resources (i.e., staff, financial assistance, workforce programs) were deployed to enhance growth in the sectors and maximize their potential.
		North Side Pittsburgh Industry and Cluster Analysis Ð ZHA conducted a cluster analysis for a significant geographic portion of the City of Pittsburgh.
		After performing a market segmentation analysis and identifying eight clusters with growth and synergy potential, ZHA developed corresponding business development, workforce development and a marketing strategy to attract and retain industry in the identified sectors.
	www.zha-inc.com /economic.htm (1002 words)

	CiteULike: Three-dimensional cluster analysis identifies interfaces and functional residue clusters in proteins. (Site not responding. Last check: )
		analysis clustering function functional protein protein_design protein_protein protein_sequence protein_structure residue_annotation residues sequence similarity spatial structure
		We evaluated 3D cluster analysis on a set of 35 families of proteins with available cocrystal structures, showing small ligand interfaces, nucleic acid interfaces and two types of protein-protein interfaces (transient and stable).
		These residue clusters correlate with specificity-conferring regions: 3D cluster analysis therefore represents an easily applied method for the prediction of functionally relevant spatial clusters of residues in proteins.
	www.citeulike.org /user/sriram_s/article/409905 (625 words)

	Cluster Analysis :: Multivariate Statistics (Statistics Toolbox)
		Cluster analysis, also called segmentation analysis or taxonomy analysis, is a way to create groups of objects, or clusters, in such a way that the profiles of objects in the same cluster are very similar and the profiles of objects in different clusters are quite distinct.
		The centroid for each cluster is the point to which the sum of distances from all objects in that cluster is minimized.
		This measure ranges from +1, indicating points that are very distant from neighboring clusters, through 0, indicating points that are not distinctly in one cluster or another, to -1, indicating points that are probably assigned to the wrong cluster.
	www.mathworks.com /access/helpdesk/help/toolbox/stats/f55357.html (3862 words)

	EPA Statistical Primer - Cluster Analysis
		Description: Cluster analysis defines groups of cases based on the similarity of multiple variables measured for each case; the algorithm picks the groups, they are not defined in advance as for DFA.
		Cluster analysis returns to you a dendritic tree, or dendogram, that shows how sites were grouped (or split) first, which next, and so on until the number of clusters you initially specified is obtained.
		On the other hand, cluster analysis does make a strong assumption that you have selected the appropriate distance measure for comparing cases.
	www.epa.gov /bioiweb1/statprimer/cluster.html (529 words)

	CiteULike: Quantification of human atherosclerotic plaques using spatially enhanced cluster analysis of ... (Site not responding. Last check: )
		CiteULike: Quantification of human atherosclerotic plaques using spatially enhanced cluster analysis of multicontrast-weighted magnetic resonance images.
		Quantification of human atherosclerotic plaques using spatially enhanced cluster analysis of multicontrast-weighted magnetic resonance images.
		Spatially enhanced cluster analysis (SECA) was performed on multicontrast MR images, and the resulting segmentation was evaluated against histological tracings.
	www.citeulike.org /user/claudiacalcagno/article/808677 (481 words)

	Multivariate Statistics: Cluster Analysis (Site not responding. Last check: )
		Cluster Analysis is a multivariate analysis technique that seeks to organize information about variables so that relatively homogenenous groups, or "clusters," can be formed.
		Although cluster analysis is relatively simple, and can use a variety of input data, it is a relatively new technique and is not supported by a comprehensive body of statistical literature.
		Some other possibilities are to look for cluster groupings that agree with existing or expected structures, or to replicate the analysis on subsets of the data to see if the structures emerge consistently.
	www.socialresearchmethods.net /tutorial/Flynn/cluster.htm (336 words)

	Cluster Analysis -- ConceptSystems.com
		The hierarchical cluster analysis is the second analysis conducted to represent the conceptual domain in concept mapping.
		This analysis is used to group individual statements on the map into clusters of statements that presumably reflect similar concepts.
		What was wanted was a cluster analysis that grouped or partitioned the statements on the map as they were placed by multidimensional scaling such that statements placed in the same cluster were in contiguous areas of the map.
	www.conceptsystems.com /kb/00000039.cfm (405 words)

	ED231A: Hierarchical Cluster Analysis
		Hierarchical cluster analysis is comprised of agglomerative methods and divisive methods that finds clusters of observations within a data set.
		Four of the better known algorithms for hierachical clustering are average linkage, complete linkage, single linkage and Ward's linkage.
		Single linkage clustering, on the other hand, computes the similarity between two groups as the similarity of the closest pair of observations between the two groups.
	www.gseis.ucla.edu /courses/ed231a1/notes2/cluster.html (533 words)

	ConClus -- Constrained Cluster Analysis
		Cluster analysis with up to 2000 variables, 45 clusters and 1 million cases.
		Confirmatorical cluster analysis: testing of a cluster structure using a second data set or testing of theoretical hypothesis.
		NEW: scale analysis with 100 variables and ULS iteration of the communalities (J”reskog), Cronbach's \alpha, the explained variance and the highest possible reliability are calclated.
	www.intext.de /concluse.htm (422 words)

	Outsource Value Cluster Analysis to India
		After this, the clusters are analyzed based on the mean values of variables used for clustering, as in the first step.
		If clusters of customers are found based on their attitudes towards new products and interest in different kinds of activities, an estimate of the segment size for each segment of the population can be obtained, by looking at the number of objects in each cluster.
		Once the number of clusters is determined, average values for each variable are determined for all the clusters, and interpretation of clusters follows.
	www.outsource2india.com /kpo/spss-data-analysis/cluster-analysis.asp (931 words)

	MMU - Bio. Sci., MSc Multivariate Statistics: Cluster analysis (Site not responding. Last check: )
		Cluster analysis (CA) is a rather loose collection of statistical methods that is used to assign cases to groups (clusters).
		In a monothetic scheme cluster membership is based on the presence or absence of a single characteristic.
		Cluster and TreeView are an integrated pair of programs for analyzing and visualizing the results of complex microarray experiments.
	obelia.jde.aca.mmu.ac.uk /multivar/ca.htm (1017 words)

	Cluster Analysis of Array Data (Site not responding. Last check: )
		ClustArray: Cluster analysis and biological interpretation of clusters.
		The clusters are output graphically, consisting or color coded renditions of the expression levels in each experiment.
		A tree can be included in the output to show the relationship between the clusters as well as the relationship between each member in a cluster.
	www.cbs.dtu.dk /services/DNAarray/cluster.php (577 words)

	Hierarchical Cluster Analysis Applied to Workers' Exposures in Fiberglass Insulation Manufacturing
		A study was conducted to explore the application of cluster analysis to the characterization of multiple exposures in industrial hygiene practice and to compare exposure groupings based on the result from cluster analysis with that based on nonmeasurement-based approaches commonly used in epidemiology.
		Cluster analysis was performed for 37 workers simultaneously exposed to endotoxin, phenolic compounds and formaldehyde in fiberglass insulation manufacturing.
		Different clustering algorithms, including complete-linkage (or farthest-neighbor), single-linkage (or nearest-neighbor), group-average, and model-based clustering approaches, were used to construct the tree structures from which clusters could be formed.
	www.infoventures.com /osh/samples/IVI-00000631.html (363 words)

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