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Survey of Clustering Algorithms
Survey of Clustering Algorithms

... few dimensions as possible. Also note that, in practice, many (predictive) vector quantizers are also used for (nonpredictive) clustering analysis [60]. Nonpredictive clustering is a subjective process in nature, which precludes an absolute judgment as to the relative efficacy of all clustering tech ...
Clustering
Clustering

... It automatically finds subspaces of the highest dimensionality such that high density clusters exist in those subspaces It is insensitive to the order of records in input and does not presume some canonical data distribution It scales linearly with the size of input and has good scalability as the n ...
Visualizing High-density Clusters in Multidimensional Data
Visualizing High-density Clusters in Multidimensional Data

Interactive textual feature selection for consensus
Interactive textual feature selection for consensus

... (must-link constraint) or in different clusters (cannot-link constraint) [17,18]. However, providing a reasonable set of constraints is a very difficult task for the users, since usually there is no prior knowledge about the spatial structure of the data [19]. The interactive feature selection is a pr ...
computational methods for learning and inference on dynamic
computational methods for learning and inference on dynamic

... creation of a model for the network that can replicate these findings. A well-known example consists of the small-world phenomenon, commonly referred to in the popular media as the “six degrees of separation.” The small-world phenomenon was demonstrated experimentally by Milgram (1967), who found th ...
NEW DENSITY-BASED CLUSTERING TECHNIQUE Rwand D. Ahmed
NEW DENSITY-BASED CLUSTERING TECHNIQUE Rwand D. Ahmed

A Fuzzy FCA-based Approach to Conceptual Clustering for Automatic Generation of Concept Hierarchy on Uncertainty Data
A Fuzzy FCA-based Approach to Conceptual Clustering for Automatic Generation of Concept Hierarchy on Uncertainty Data

Data Clustering: A Review - Research in Data Clustering
Data Clustering: A Review - Research in Data Clustering

Ant-based Clustering Algorithms: A Brief Survey
Ant-based Clustering Algorithms: A Brief Survey

Lecture Notes - Computer Science Department
Lecture Notes - Computer Science Department

... will result on a set of attributes that describe what interest us from the domain and what we want to use for the discovery process. According to this representation we can categorize the different kinds of datasets in two groups. The first kind are those that can be represented by a flat structure, ...
Trajectory Clustering: A Partition-and-Group Framework
Trajectory Clustering: A Partition-and-Group Framework

... We contend that discovering the common sub-trajectories is very useful, especially if we have regions of special interest for analysis. In this case, we want to concentrate on the common behaviors within those regions. There are many examples in real applications. We present two application scenario ...
Extending the Data Mining Software Packages SAS Enterprise
Extending the Data Mining Software Packages SAS Enterprise

... distinct groups of data such as a cluster of affluent people who are married with teen age children. The business might then look more closely at this group and find that they are more profitable than other segments, yet they have a much higher attrition rate. Further investigation might yield that ...
Representative Clustering of Uncertain Data
Representative Clustering of Uncertain Data

... Clustering is undoubtedly one of the most important tools for unsupervised classification. A large number of clustering algorithms has been developed, as reflected in numerous surveys [28, 34, 45]. Although clustering has proved its applicability in many different domains and scenarios, the problem ...
Representative Clustering of Uncertain Data
Representative Clustering of Uncertain Data

... Clustering is undoubtedly one of the most important tools for unsupervised classification. A large number of clustering algorithms has been developed, as reflected in numerous surveys [28, 34, 45]. Although clustering has proved its applicability in many different domains and scenarios, the problem ...
symbiotic evolutionary subspace clustering (s-esc)
symbiotic evolutionary subspace clustering (s-esc)

Discovery of Climate Indices using Clustering
Discovery of Climate Indices using Clustering

... observed that in some years the warm southward current, which appeared around Christmas, would persist for an unusually long time, with a disastrous impact on fishing. In the early 20th century, while studying the trade winds and Indian monsoon, scientists noticed large scale changes in pressure in ...
Discovery of Climate Indices using Clustering,
Discovery of Climate Indices using Clustering,

Data clustering: 50 years beyond K-means
Data clustering: 50 years beyond K-means

Nearest Neighbour - University of Houston
Nearest Neighbour - University of Houston

Finding Non-Redundant, Statistically Signi cant Regions in
Finding Non-Redundant, Statistically Signi cant Regions in

Nearest Neighbour - Department of Computer Science
Nearest Neighbour - Department of Computer Science

Applying Semantic Analyses to Content
Applying Semantic Analyses to Content

... – Used Tweets about NA, Asia, Africa and Europe – Comparable performance for ESA and Probase Conceptualization ...
Iterative Discovery of Multiple Alternative Clustering Views
Iterative Discovery of Multiple Alternative Clustering Views

... of these goals can be formulated as clustering problems, but existing clustering methods are often not sufficiently flexible to cover the range of desired data analytic goals. In particular, most clustering algorithms find only a single partitioning of the data [1], but data items can often be group ...
H. Wang, H. Shan, A. Banerjee. Bayesian Cluster Ensembles
H. Wang, H. Shan, A. Banerjee. Bayesian Cluster Ensembles

Transaction / Regular Paper Title
Transaction / Regular Paper Title

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Human genetic clustering



Human genetic clustering analysis uses mathematical cluster analysis of the degree of similarity of genetic data between individuals and groups in order to infer population structures and assign individuals to groups. These groupings in turn often, but not always, correspond with the individuals' self-identified geographical ancestry. A similar analysis can be done using principal components analysis, which in earlier research was a popular method. Many studies in the past few years have continued using principal components analysis.
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