Improved text classification algorithm for spam filtering based on CABSOFV

Clustering Algorithm Based on Sparse Feature Vector without Specifying Parameter

“…Hence, CABOSFV algorithm is of high computing efficiency and good clustering performance [15]. Subsequently, CABOSFV has attracted extensive attention, and it is exercised in many applications such as customer knowledge discovery [15], text mining [16,17], traditional Chinese medicine [18] and Intelligent Miner (I-MINER) [19].…”

Improved Bidirectional CABOSFV Based on Multi-Adjustment Clustering and Simulated Annealing

“…Classic CABOSFV uses Sparse Feature Dissimilarity (SFD) to describe the dissimilarity between sets; it uses Sparse Feature Vector (SFV) to extract features of the set, to reduce data scale, and then to implement clustering by addition of SFV. Classic CABOSFV is insensitive to noise, it is available to cluster both sparse and dense high dimensional data, and has helped solving a series of high dimensional data clustering problems [4][5][6][7][8][9].…”