Automatic Subspace Clustering of High Dimensional Data
نویسندگان
چکیده
منابع مشابه
Automatic Subspace Clustering of High Dimensional Data for DataMining
Data mining applications place special requirements on clustering algorithms including: the ability to nd clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records. We present CLIQUE, a clustering algorithm that satisses each of these require...
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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...
متن کاملSoft Subspace Clustering for High-Dimensional Data
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...
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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...
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ژورنال
عنوان ژورنال: Data Mining and Knowledge Discovery
سال: 2005
ISSN: 1384-5810,1573-756X
DOI: 10.1007/s10618-005-1396-1