Clustering for High Dimensional Data: Density based Subspace Clustering Algorithms
نویسندگان
چکیده
منابع مشابه
Clustering for High Dimensional Data: Density based Subspace Clustering Algorithms
Finding clusters in high dimensional data is a challenging task as the high dimensional data comprises hundreds of attributes. Subspace clustering is an evolving methodology which, instead of finding clusters in the entire feature space, it aims at finding clusters in various overlapping or non-overlapping subspaces of the high dimensional dataset. Density based subspace clustering algorithms t...
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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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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...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2013
ISSN: 0975-8887
DOI: 10.5120/10584-5732