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Among the bio-inspired techniques, PSO-based clustering algorithms have received special attention. An improved method named Particle Swarm Optimization (PSO) clustering algorithm based on cooperative evolution with multi-populations was presented. It adopts cooperative evolutionary strategy with multi-populations to change the mode of traditional searching optimum solutions. It searches the local optimum and updates the whole best position (gBest) and local best position (pBest) ceaselessly. The gaest will be passed in all sub-populations. When the gBest meets the precision, the evolution will terminate. The whole clustering process is divided into two stages. The first stage uses the cooperative evolutionary PSO algorithm to search the initial clustering centers. The second stage uses the K-meaus algorithm. The experiment results demonstrate that this method can extract the correct number of clusters with good clustering quality compared with the results obtained from other clustering algorithms. [ABSTRACT FROM AUTHOR]

This paper proposed an Apriori frequent itemset mining based on bittable and inverted index (A-FIMBII). A-FIM-BII built the index from the collection of items to the transaction, and used priori nature to reduce the generation of candidate sets, calculating the support counts of two items through the bit operations. It compared to Apriori, ECLAT in four datasets. The experiment results show that A-FIMBII has a higher efficiency. [ABSTRACT FROM AUTHOR]