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31 
Largescale clustering: algorithms and applicationsGuan, Yuqiang 28 August 2008 (has links)
Not available / text

32 
Correspondence analysis and clustering with applications to sitespecies occurrence梁德貞, Leung, Takching. January 1991 (has links)
published_or_final_version / Statistics / Master / Master of Philosophy

33 
Unsupervised learning algorithms applied to data analysisAmsel, Rhonda Toppston January 1977 (has links)
No description available.

34 
Extending linear grouping analysis and robust estimators for very large data setsHarrington, Justin 11 1900 (has links)
Cluster analysis is the study of how to partition data into homogeneous subsets so that the partitioned data share some common characteristic. In one to three dimensions, the human eye can distinguish well between clusters of data if clearly separated. However, when there are more than three dimensions and/or the data is not clearly separated, an algorithm is required which needs a metric of similarity that quantitatively measures the characteristic of interest.
Linear Grouping Analysis (LGA, Van Aelst et al. 2006) is an algorithm for clustering data around hyperplanes, and is most appropriate when: 1) the variables are related/correlated, which results in clusters with an approximately linear structure; and
2) it is not natural to assume that one variable is a “response”, and the remainder the “explanatories”.
LGA measures the compactness within each cluster via the sum of squared orthogonal distances to hyperplanes formed from the data.
In this dissertation, we extend the scope of problems to which LGA can be applied. The first extension relates to the linearity requirement inherent within LGA, and proposes a new method of nonlinearly transforming the data into a Feature Space, using the Kernel Trick, such that in this space the data might then form linear clusters. A possible side effect of this transformation is that the dimension of the transformed space is significantly larger than the number of observations in a given cluster, which causes problems with orthogonal regression. Therefore, we also introduce a new method for calculating the distance of an observation to a cluster when its covariance matrix is rank deficient.
The second extension concerns the combinatorial problem for optimizing a LGA objective function, and adapts an existing algorithm, called BIRCH, for use in providing fast, approximate solutions, particularly for the case when data does not fit in memory. We also provide solutions based on BIRCH for two other challenging optimization problems in the field of robust statistics, and demonstrate, via simulation study as well as application on actual data sets, that the BIRCH solution compares favourably to the existing stateoftheart alternatives, and in many cases finds a more optimal solution.

35 
Person and situation subgroup membership as predictive of job performance and job perceptionsGustafson, Sigrid Beda 12 1900 (has links)
No description available.

36 
Extending linear grouping analysis and robust estimators for very large data setsHarrington, Justin 11 1900 (has links)
Cluster analysis is the study of how to partition data into homogeneous subsets so that the partitioned data share some common characteristic. In one to three dimensions, the human eye can distinguish well between clusters of data if clearly separated. However, when there are more than three dimensions and/or the data is not clearly separated, an algorithm is required which needs a metric of similarity that quantitatively measures the characteristic of interest.
Linear Grouping Analysis (LGA, Van Aelst et al. 2006) is an algorithm for clustering data around hyperplanes, and is most appropriate when: 1) the variables are related/correlated, which results in clusters with an approximately linear structure; and
2) it is not natural to assume that one variable is a “response”, and the remainder the “explanatories”.
LGA measures the compactness within each cluster via the sum of squared orthogonal distances to hyperplanes formed from the data.
In this dissertation, we extend the scope of problems to which LGA can be applied. The first extension relates to the linearity requirement inherent within LGA, and proposes a new method of nonlinearly transforming the data into a Feature Space, using the Kernel Trick, such that in this space the data might then form linear clusters. A possible side effect of this transformation is that the dimension of the transformed space is significantly larger than the number of observations in a given cluster, which causes problems with orthogonal regression. Therefore, we also introduce a new method for calculating the distance of an observation to a cluster when its covariance matrix is rank deficient.
The second extension concerns the combinatorial problem for optimizing a LGA objective function, and adapts an existing algorithm, called BIRCH, for use in providing fast, approximate solutions, particularly for the case when data does not fit in memory. We also provide solutions based on BIRCH for two other challenging optimization problems in the field of robust statistics, and demonstrate, via simulation study as well as application on actual data sets, that the BIRCH solution compares favourably to the existing stateoftheart alternatives, and in many cases finds a more optimal solution.

37 
Cluster analysis for market segmentation /Brandt, Angela, January 2005 (has links)
Thesis (M.A.)University of Toronto, 2005. / Includes bibliographical references (leaves 4851).

38 
On detection of extreme data points in cluster analysis /Soon, Shih Chung, January 1987 (has links)
Thesis (Ph. D.)Ohio State University, 1987. / Includes vita. Includes bibliographical references (leaves 260274). Available online via OhioLINK's ETD Center.

39 
Clustering algorithms for categorical data /Andreopoulos, William. January 2006 (has links)
Thesis (Ph.D.)York University, 2006. Graduate Programme in Computer Science and Engineering. / Typescript. Includes bibliographical references (leaves 297328). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.882004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:NR19856

40 
"Clustering categorical response" application to lung cancer problems in living scalesGuo, Ling. January 2008 (has links)
Thesis (M.S.)Georgia State University, 2008. / Title from file title page. Jiawei Liu, Yusheng Hsu, committee cochairs; Jeff Qin, committee member. Electronic text (82 p. : ill. (some col.)) : digital, PDF file. Description based on contents viewed Aug. 20, 2008. Includes bibliographical references (p. 6566).

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