Exploring cluster analysis

Exploring the Landscape of Clusterings: towards Better Integration

[Proposal defense document-submitted for review to the PhD dissertation committee]

Clustering Stream Data by Exploring the Evolution of Density Mountain

Stream clustering is a fundamental problem in many streaming data analysis applications. Comparing to classical batchmode clustering, there are two key challenges in stream clustering: (i) Given that input data are changing continuously, how to incrementally update clustering results efficiently? (ii) Given that clusters continuously evolve with the evolution of data, how to capture the cluster...

Computational Intelligence in Clustering Algorithms, With Applications

Cluster analysis plays an important role for understanding various phenomena and exploring the nature of obtained data. A remarkable diversity of ideas, in a wide range of disciplines, has been applied to clustering research. Here, we survey clustering algorithms in computational intelligence, particularly based on neural networks and kernel-based learning. We further illustrate their applicati...

A Document Exploring System on Lda Topic Model for Wikipedia Articles

A Large number of digital text information is generated every day. Effectively searching, managing and exploring the text data has become a main task. In this paper, we first present an introduction to text mining and LDA topic model. Then we deeply explained how to apply LDA topic model to text corpus by doing experiments on Simple Wikipedia documents. The experiments include all necessary ste...

Feature selection for clustering using instance-based learning by exploring the nearest and farthest neighbors