Aiming at the problem of slow speed of the existing model-based Multivariate Time Series (MTS) clustering algorithm when dealing with MTS wtih unequal length, an improved clustering algorithm named MUltivariate Time Series Clustering Algorithm based on Lift Ratio (LR) Component Extraction (MUTSCA〈LRCE〉) was proposed. Firstly, the equal frequency discretization method was used to symbolize MTS. Then, the LR vector was calculated to express the temporal pattern between the dimensions of time series of MTS samples. Each LR vector was sorted and a fixed number of different key components were extracted from both ends. All the extracted key components were spliced to form a model vector for representing the MTS samples. The MTS sample set with unequal length was transformed into a model vector set with equal length. Finally, the k-means algorithm was used for the clustering analysis of generated model vector set with equal length. The experimental results on multiple common data sets show that, compared with the model-based MTS clustering algorithm named MUltivariate Time Series Clustering Algorithm〈LR〉(MUTSCA〈LR〉), the proposed algorithm can significantly improve the clustering speed of MTS data sets with unequal length under the premise of guaranteeing clustering effect.

In order to improve the efficiency of building Fuzzy Associative Classifier (FAC) on the dynamic data sets, an incremental fuzzy associative classification method based on eVQ (evolving Vector Quantization) clustering algorithm was proposed. Firstly, eVQ clustering algorithm was adopted to incrementally update the parameters of Gauss membership functions of quantitative attributes. Secondly, Update With Early Pruning (UWEP) algorithm was extended to incrementally mine fuzzy frequent itemsets. Finally, Fuzzy CORRelation (FCORR) of Fuzzy Associative Classification Rule (FACR) and the length of antecedent of FACR were regarded as measures to prune and update fuzzy associative classification rule base. The experimental results on four UCI benchmark data sets show that compared with the batch fuzzy association classification modeling method, the proposed method can reduce the time of training the FAC in the premise of not decreasing the accuracy and interpretability. The Gauss membership function updating method based on eVQ clustering algorithm contributes to improve the classification accuracy of the FAC on the dynamic data sets.