Information-Theoretic Based Clustering Method for High-Dimensional Data
نویسندگان
چکیده
منابع مشابه
Hierarchical Information-theoretic Co-clustering for High Dimensional Data
Hierarchical clustering is an important technique for hierarchical data exploration applications. However, most existing hierarchial methods are based on traditional one-side clustering, which is not effective for handling high dimensional data. In this paper, we develop a partitional hierarchical co-clustering framework and propose a Hierarchical Information-Theoretical Co-Clustering (HITCC) a...
متن کاملHigh-Dimensional Clustering Method for High Performance Data Mining
Many clustering methods are not suitable as high-dimensional ones because of the so-called ‘curse of dimensionality’ and the limitation of available memory. In this paper, we propose a new high-dimensional clustering method for the high performance data mining. The proposed high-dimensional clustering method provides efficient cell creation and cell insertion algorithms using a space-partitioni...
متن کاملClustering 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...
متن کاملEntropy-based Consensus for Distributed Data Clustering
The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...
متن کاملPCS: An Efficient Clustering Method for High-Dimensional Data
Clustering algorithms play an important role in data analysis and information retrieval. How to obtain a clustering for a large set of highdimensional data suitable for database applications remains a challenge. We devise in this paper a set-theoretic clustering method called PCS (Pairwise Consensus Scheme) for high-dimensional data. Given a large set of d-dimensional data, PCS first constructs...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Physics: Conference Series
سال: 2020
ISSN: 1742-6588,1742-6596
DOI: 10.1088/1742-6596/1533/2/022115