Subspace clustering of high-dimensional data: a predictive approach
نویسندگان
چکیده
منابع مشابه
Subspace Clustering of High Dimensional Data
Clustering suffers from the curse of dimensionality, and similarity functions that use all input features with equal relevance may not be effective. We introduce an algorithm that discovers clusters in subspaces spanned by different combinations of dimensions via local weightings of features. This approach avoids the risk of loss of information encountered in global dimensionality reduction tec...
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A fundamental operation in data mining is to partition a given dataset into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some defined criteria [2]. These criteria are usually defined in the form of some distance, and similarity is hence defined as follows, the smaller the distance is, the more similar the objects a...
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High dimensional data is a phenomenon in real-world data mining applications. Text data is a typical example. In text mining, a text document is viewed as a vector of terms whose dimension is equal to the total number of unique terms in a data set, which is usually in thousands. High dimensional data occurs in business as well. In retails, for example, to effectively manage supplier relationshi...
متن کاملClustering high dimensional data using subspace and projected clustering algorithms
Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerates clusters of objects in all subspaces of a dataset. It tends to produce many over lapping clusters. Approach: Subspace clustering and projected clustering are research areas for clustering in high dimensional spaces. I...
متن کاملA Preview on Subspace Clustering of High Dimensional Data
When clustering high dimensional data, traditional clustering methods are found to be lacking since they consider all of the dimensions of the dataset in discovering clusters whereas only some of the dimensions are relevant. This may give rise to subspaces within the dataset where clusters may be found. Using feature selection, we can remove irrelevant and redundant dimensions by analyzing the ...
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ژورنال
عنوان ژورنال: Data Mining and Knowledge Discovery
سال: 2013
ISSN: 1384-5810,1573-756X
DOI: 10.1007/s10618-013-0317-y