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 ...
... 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
... 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 ...
... 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 ...
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 ...
... (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
... 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 ...
... 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 ...
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, ...
... 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
... 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 ...
... 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
... 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 ...
... 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
... 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 ...
... 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
... 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 ...
... 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 ...
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 ...
... 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 ...
Applying Semantic Analyses to Content
... – Used Tweets about NA, Asia, Africa and Europe – Comparable performance for ESA and Probase Conceptualization ...
... – Used Tweets about NA, Asia, Africa and Europe – Comparable performance for ESA and Probase Conceptualization ...
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 ...
... 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 ...
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.