We propose a fuzzy based method for multilabel text classification in which a document can belong to one or more than one category. In text categorization, the number of the involved features is usually huge, causing the curse of the dimensionality problem. Besides, a category can be a nonconvex region, which is a union of several overlapping or disjoint subregions. An automatic classification system, thus, may suffer from large memory requirements or poor performance. By incorporating fuzzy techniques, our proposed method can overcome these issues. A fuzzy relevance measure is adopted to transform high-dimensional documents to low-dimensional fuzzy relevance vectors to avoid the curse of dimensionality problem. A clustering technique is used to divide the relevance space into a collection of subregions which are then combined to make up individual categories. This allows complex and nonconvex regions to be created. A number of experiments are presented to show the effectiveness of the proposed method in both performance and speed.

Measuring the similarity between documents is an important operation in the text processing field. In this paper, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature, the proposed measure takes the following three cases into account: a) The feature appears in both documents, b) the feature appears in only one document, and c) the feature appears in none of the documents. For the first case, the similarity increases as the difference between the two involved feature values decreases. Furthermore, the contribution of the difference is normally scaled. For the second case, a fixed value is contributed to the similarity. For the last case, the feature has no contribution to the similarity. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems. The results show that the performance obtained by the proposed measure is better than that achieved by other measures.

Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. In this paper, we propose a fuzzy similarity-based self-constructing algorithm for feature clustering. The words in the feature vector of a document set are grouped into clusters, based on similarity test. Words that are similar to each other are grouped into the same cluster. Each cluster is characterized by a membership function with statistical mean and deviation. When all the words have been fed in, a desired number of clusters are formed automatically. We then have one extracted feature for each cluster. The extracted feature, corresponding to a cluster, is a weighted combination of the words contained in the cluster. By this algorithm, the derived membership functions match closely with and describe properly the real distribution of the training data. Besides, the user need not specify the number of extracted features in advance, and trial-and-error for determining the appropriate number of extracted features can then be avoided. Experimental results show that our method can run faster and obtain better extracted features than other methods.

We propose a new representation of calendars such that users can specify fuzzy calendar constraints to discover asynchronous periodical association rules embedded in temporal databases. We borrow the fuzzy set theory and use the conjunction operation to construct fuzzy calendar patterns and each fuzzy calendar pattern represents an asynchronous periodical behavior. Moreover, different time intervals have different weights corresponding to their matching degrees to the specified fuzzy calendar pattern. An efficient algorithm is also proposed to find association rules with the specified fuzzy calendar pattern. Unlike level wise a priori-based approaches, our method scans the underlying database at most twice. In the first scan, frequent 2-itemsets with their weighted counts in the specified fuzzy calendar pattern are obtained and then all candidate item sets are generated from the discovered frequent 2-itemsets. Finally, all frequent item sets with their weighted counts in the specified fuzzy calendar pattern are discovered in one shot. Asynchronous periodical association rules in the specified fuzzy calendar pattern are then obtained