Clustering via dimensional reduction method for the projection pursuit based on the ICSA |
| |
Authors: | Shuiping Gou Jing Feng Licheng Jiao |
| |
Affiliation: | Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education, Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China |
| |
Abstract: | The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make
clustering method limited in many application. To solve this problem, clustering method with Projection Pursuit dimension
reduction based on Immune Clonal Selection Algorithm (ICSA-PP) is proposed in this paper. Projection pursuit strategy can
maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search
optimizing projection direction. The proposed algorithm can converge quickly with less iteration to reduce dimension of some
high-dimensional datasets, and in which space, K-mean clustering algorithm is used to partition the reduced data. The experiment
results on UCI data show that the presented method can search quicker to optimize projection direction than Genetic Algorithm
(GA) and it has better clustering results compared with traditional linear dimension reduction method for Principle Component
Analysis (PCA). |
| |
Keywords: | Projection Pursuit (PP) Immune Clonal Selection Algorithm (ICSA) Genetic Algorithm (GA) K-means clustering |
本文献已被 维普 万方数据 SpringerLink 等数据库收录! |