Abstract：The assumption that all data features are equally important in the categorical data-sequential information bottleneck(CD-sIB) lowers the transformation quality. A weighting binary transformation method is proposed to reveal the feature of non co-occurrence data by highlighting the representative features and depressing the redundancy features. Meanwhile,two weighting rules,the applicability of stochastically distributed data and the non supervision of weighting schemes,are introduced. Then,the weighted categorical data-sequential information bottleneck(WCD-sIB) algorithm is presented based on the weighting granularity concept. The experimental results show that the weighting binary transformation method generates good co-occurrence data representation,and the WCD-sIB algorithm is superior to the other algorithms.
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