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A Distributed Weighted Possibilistic c-Means Algorithm for Clustering Incomplete Big Sensor Data
2014
International Journal of Distributed Sensor Networks
Possibilistic c-means clustering algorithm (PCM) has emerged as an important technique for pattern recognition and data analysis. Owning to the existence of many missing values, PCM is difficult to produce a good clustering result in real time. The paper proposes a distributed weighted possibillistic c-means clustering algorithm (DWPCM), which works in three steps. First the paper applies the partial distance strategy to PCM (PDPCM) for calculating the distance between any two objects in the
doi:10.1155/2014/430814
fatcat:cijdzrcyhzg4zfzeka4p4il3u4