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In recent years, time series subsequence clustering is often reported to be very efficient in the data mining community. However, in this article, a negative justification will be given. The overall theoretical analysis and experiments on counterexamples indicate that time series subsequence clustering almost always does not yield any useful result.

Context windows are important for a variety of natural language analysis and processing. A trade-off exists between the task performance and the size of the context. Lucassen and Mercer used mutual information to determine the size of the context for English text. We apply the same technique to determine the Context window size for Chinese text. In addition, we use the association score, proposed by Church. The association score is directly related to the prediction ability of units in the context. To reduce the effects of spurious associations, the association score values at the N% quartile is used, instead of the maximum, and the association score derived from low frequency occurrences (i.e.

In this paper, a new classification method (ADCC) for high-dimensional data is proposed. In this method, a decision cluster classification (DCC) model consists of a set of disjoint decision clusters, each labeled with a dominant class that determines the class of new objects falling in the cluster. A cluster tree is first generated from a training data set by recursively calling a variable weighting k-means algorithm. Then, the DCC model is extracted from the tree. Various tests including Anderson–Darling test are used to determine the stopping condition of the tree growing. A series of experiments on both synthetic and real data sets have been conducted. Their results show that the new classification method (ADCC) performed better in accuracy and scalability than existing methods like k-NN, decision tree and SVM. ADCC is particularly suitable for large, high-dimensional data with many classes.