Classical text clustering algorithms are usually based on vector space model or its variants. Because of the high computing complexity and the difficulty of controlling clustering results, this kind of approaches are hard to be applied for the purpose of the large scale text clustering. Clustering algorithms based on frequent term sets make use of relationship among documents and their shared frequent term sets to achieve high accuracy and effectiveness in clustering. But since the number of frequent terms is usually too large to reach the efficiency requirement for large collection texts clustering, this paper proposes a novel text clustering approach based on maximal frequent term sets (MFTSC). This approach firstly mines maximal frequent term sets from text set and then clusters texts by following steps: at first, the maximal frequent term sets are clustered based on the criterion of k-mismatch; then texts are clustered according to term sets clustering results; finally, we categorize the left texts uncovered in previous step into produced text clusters Be compared with existing approaches, our experimental results show an average gain of 10% on F-Measure score with better performance on scalability and efficiency.

Text clustering techniques were usually used to structure the text documents into topic related groups which can facilitate users to get a comprehensive understanding on corpus or results from information retrieval system. Most of existing text clustering algorithm which derived from traditional formatted data clustering heavily rely on term analysis methods and adopted vector space model (VSM) as their document representation. But because of the essential characteristic underlying text such as high dimensionality features vector space, the problem of sparseness has a strong impact on the clustering algorithm. So feature reduction is an important preprocess step for improving the efficiency and accuracy of clustering algorithm by removing redundant and irrelevant terms from corpus. Even the clustering is considered as an unsupervised learning method, but in text, there is still some priori knowledge we can use from NLP analysis based approach. In this paper, we propose a semantic analysis based feature reduction method which used in Chinese text clustering. Our method bases on a dedicated Part-of-Speech tags selection and synonyms consolidation and can reduce the feature space of documents more effectively compared with traditional feature reduction method tfidf and stopwords removal; meanwhile it preserves or sometimes even improves the accuracy of clustering algorithm. In our experiment, we tested our feature reduction method using bisecting k-means algorithm which was proved be efficient in text clustering. The results show that our method can reduce the feature space significantly, and meanwhile have a better clustering accuracy in terms of the purity.

Though link analysis based page ranking approaches have reached great success in commercial search engines (SE), the content based relevance computing approaches also play a very important role in the ranking of information retrieval results. Since most of existing relevance computing algorithms are running on the full text of a web page, this paper is focused on the relevance computing between user’s query and the auto-generated text summarization of each webpage. The first part of this paper provides a brief introduction of the state of art of relevance computing in SE. The inference network approach is especially concerned in this paper since it is the baseline method in our experiment SE system. Then the auto text summarization method based on multi-source integration is introduced, and the full text of each web page is replaced by its auto-generated abstract to compute the relevance between the webpage and user query. To evaluate the effect of the condensation representation of full text on the relevance based page rank of a system, several experiments are conducted in the last part of this paper, which include the method remarked above with different compress ratio, and the full text based ranking. In addition to the efficiency gain of the SE system, the experiment results also shows that the ranking results based on the summary generated by our text summarization system with 30% compress ratio can also get 11.29% of the precision improvement for the SE system.

The paper presents the principles of two quadrants control, such as the traction control and regenerative braking control. The configuration of the switched reluctance motor drive for an electric drive bicycle, the schematic diagram of the system, the motor, the rotor position detector, the control logic for the operation at two quadrants and the two quadrants control scheme, are given. A comparison is given of the two types of the three-phase motors, such as the 12/8 and 6/8 structure motors, at the same average electromagnetic torque in the turn numbers of per stator pole coil, the resistance of the stator phased windings, and the basic output frequency of the power converter. Tested results of the developed prototype with the three-phase 12/8 structure motor, in the travel course with once charging, in the velocity, in the output torque, in the output power, in the systematic efficiency and in the peak value of phase current, are presented.