A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
ACM Computing Surveys (CSUR)
New algorithms for efficient mining of association rules
Information Sciences: an International Journal
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
High-performance data mining with skeleton-based structured parallel programming
Parallel Computing - Parallel data-intensive algorithms and applications
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
On distributing the clustering process
Pattern Recognition Letters
Parallelization of Decision Tree Algorithm and its Performance Evaluation
HPC '00 Proceedings of the The Fourth International Conference on High-Performance Computing in the Asia-Pacific Region-Volume 2 - Volume 2
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Distributed data mining on grids: services, tools, and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Parallel nonlinear optimization techniques for training neural networks
IEEE Transactions on Neural Networks
Modified self-organizing map for optical flow clustering system
SSIP'07 Proceedings of the 7th WSEAS International Conference on Signal, Speech and Image Processing
A Load Balancing Knapsack Algorithm for Parallel Fuzzy c-Means Cluster Analysis
High Performance Computing for Computational Science - VECPAR 2008
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This work presents an implementation of a parallel Fuzzy c-means cluster analysis tool, which implements both aspects of cluster investigation: the calculation of clusters' centers with the degrees of membership of records to clusters, and the determination of the optimal number of clusters for the data, by using the PBM validity index to evaluate the quality of the partition. The work's main contributions are the implementation of the entire cluster's analysis process, which is a new approach in literature, integrating to clusters calculation the finding of the best natural pattern present in data, and also, the parallel processing implementation of this tool, which enables this approach to be used with vary large volumes of data, a increasing need for data analysis in nowadays industries and business databases, making the cluster analysis a feasible tool to support specialist's decision in all fields of knowledge. The results presented in the paper show that this approach is scalable and brings processing time reduction as an benefit that parallel processing can bring to the matter of cluster analysis.