Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
On distributing the clustering process
Pattern Recognition Letters
Parallel fuzzy c-means cluster analysis
VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This work proposes a load balance algorithm to parallel processing based on a variation of the classical knapsack problem. The problem considers the distribution of a set of partitions, defined by the number of clusters, over a set of processors attempting to achieve a minimal overall processing cost. The work is an optimization for the parallel fuzzy c-means (FCM) clustering analysis algorithm proposed in a previous work composed by two distinct parts: the cluster analysis, properly said, using the FCM algorithm to calculate of clusters centers and the PBM index to evaluate partitions, and the load balance, which is modeled by the multiple knapsack problem and implemented through a heuristic that incorporates the restrictions related to cluster analysis in order to gives more efficiency to the parallel process.