A Survey on Soft Subspace Clustering

Subspace clustering (SC) is a promising clustering technology to identify clusters based on their associations with subspaces in high dimensional spaces. SC can be classified into hard subspace clustering (HSC) and soft subspace clustering (SSC). While HSC algorithms have been extensively studied and well accepted by the scientific community, SSC algorithms are relatively new but gaining more attention in recent years due to better adaptability. In the paper, a comprehensive survey on existing SSC algorithms and the recent development are presented. The SSC algorithms are classified systematically into three main categories, namely, conventional SSC (CSSC), independent SSC (ISSC) and extended SSC (XSSC). The characteristics of these algorithms are highlighted and the potential future development of SSC is also discussed.Comment: This paper has been published in Information Sciences Journal in 201

Automated construction of a hierarchy of self-organized neural network classifiers

This paper documents an effort to design and implement a neural network-based, automatic classification system which dynamically constructs and trains a decision tree. The system is a combination of neural network and decision tree technology. The decision tree is constructed to partition a large classification problem into smaller problems. The neural network modules then solve these smaller problems. We used a variant of the Fuzzy ARTMAP neural network which can be trained much more quickly than traditional neural networks. The research extends the concept of self-organization from within the neural network to the overall structure of the dynamically constructed decision hierarchy. The primary advantage is avoidance of manual tedium and subjective bias in constructing decision hierarchies. Additionally, removing the need for manual construction of the hierarchy opens up a large class of potential classification applications. When tested on data from real-world images, the automatically generated hierarchies performed slightly better than an intuitive (handbuilt) hierarchy. Because the neural networks at the nodes of the decision hierarchy are solving smaller problems, generalization performance can really be improved if the number of features used to solve these problems is reduced. Algorithms for automatically selecting which features to use for each individual classification module were also implemented. We were able to achieve the same level of performance as in previous manual efforts, but in an efficient, automatic manner. The technology developed has great potential in a number of commercial areas, including data mining, pattern recognition, and intelligent interfaces for personal computer applications. Sample applications include: fraud detection, bankruptcy prediction, data mining agent, scalable object recognition system, email agent, resource librarian agent, and a decision aid agent

Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs

Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clustering methods for challenging unsupervised machine learning problems are also discussed.Comment: 13 figures, 35 reference