Using Self-Similarity to Incorporate Dimensionality Reduction and Cluster Evolution Tracking

Abstract:

Cluster evolution tracking and dimensionality reduction have been studied intensively but separately in the time decayed and high dimensional stream data environment during the past decades. However, the interaction between the cluster evolution and the dimensionality reduction is the most common scenario in the time decayed stream data. Therefore, the dimensionality reduction should interact with cluster operation in the endless life cycle of stream data. In this paper, we first investigate the interaction between dimensionality reduction and cluster evolution in the high dimensional time decayed stream data. Then, we integrate the on-line sequential forward fractal dimensionality reduction technique with self-adaptive technique for cluster evolution tracking based on multi-fractal. Our performance experiments over a number of real and synthetic data sets illustrate the effectiveness and efficiency provided by our approach.

• Periodical: Applied Mechanics and Materials (Volumes 336-338)
• Pages: 2242-2247
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.336-338.2242
• Online since: July 2013
• Authors: Guang Hui Yan, Yong Chen, Hong Yun Zhao, Ya Jin Ren, Zhi Cheng Ma
• Keywords: Adaptive Sample Technique, Cluster Evolution, Data Mining (DM), Dimensionality Reduction, Self-Similarity

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