Focusing on the problem that PrePost algorithm needs to build complex Pre-order and Post-order Code tree (PPC-tree) and Node list (N-list), an improved frequent itemset mining algorithm based on the Interval list (I-list) was proposed. Firstly, data storage structure with more compression compared to Frequent Pattern tree (FP-tree), called Interval FP-tree (IFP-tree), was adopted, which mined frequent itemsets without iteratively establishing conditional tree. Secondly, the more concise method called I-list was used to replace the complex N-list in PrePost so as to improve mining speed. Finally, in the case of single branch path, some frequent itemsets were directly obtained by the method of combination. The experimental results prove the correctness of the proposed algorithm by getting the same results for the same dataset under same minimum supports, the proposed algorithm is superior to PrePost algorithm by about 10 percent in terms of time and space which has a good application in sparse database or intensive database.

With the continuous development of enterprise marketing, the enterprise abbreviation has been widely used. Nevertheless, as one of the main sources of unknown words, the enterprise abbreviation can not be effectively identified. A methodology on predicting enterprise abbreviation based on constitution pattern and Conditional Random Field (CRF) was proposed. First, the constitution patterns of enterprise name and abbreviation were summarized from the perspective of linguistics, and the Bi-gram algorithm was improved by a combination of lexicon and rules, namely CBi-gram. CBi-gram algorithm was used to realize the automatic segmentation of the enterprise name and improve the recognition accuracy of the company's core word. Then the enterprise type was subdivided by CBi-gram, and the abbreviation rule sets were collected by artificial summary and self-learning method to reduce noise caused by unsuitable rules. Besides, in order to make up the limitations of artificial building rules on abbreviations and mixed abbreviation, the CRF was introduced to generate enterprise abbreviation statistically, and word, tone and word position were used as characteristics to train model as supplementary. The experimental results show that the method exhibites a good performance and the output can fundamentally cover the usual range of enterprise abbreviations.

k-Nearest Neighbor ( kNN) algorithm is commonly used in data imputation. It is of poor efficiency because of the similarity computation between every tow records. To solve the efficiency problem, an improved kNN data imputation algorithm combined with Locality Sensitive Hashing (LSH) named LSH- kNN was proposed. First, all the complete records were indexed in LSH way. Then corresponding LSH ways for nominal, numeric and mixed-type incomplete data were put forward, and LSH values for all the incomplete records were computed in the proposed way to find candidate similar records. Finally, the incomplete records' real distance to candidate similar records were calculated, and the top- k similar records for kNN imputation were found. The experimental results show that the proposed method LSH- kNN has higher efficiency than traditional kNN as well as keeping almost the same accuracy.

Concerning the problem of the decline of 3GPP Long Term Evolution (LTE) system throughput caused by the frequent handover, a location prediction model based on user movement mechanism and changing probability of direction was proposed. The proposed model was combined with the standard handover algorithm of LTE. The model calculated the weight of each possible location using the changing probability of direction, then got the predicted received signal strength through summing. The simulation results show that the number of handover does not change obviously, but the system throughput is improved after using the combined algorithm. In addition, the proposed model is better than the traditional data mining prediction model.

The paper incorporated the multifractal analysis method into the idea of Quaternion Wavelet Transform (QWT), which took advantage of the rotation-invariant properties and multifractal properties of texture image, and could make up for the lacks of ability to decompose input image into multiple orientation in texture classification when using wavelet transform. The experiment of texture classification using the images from UIUC shows the method has higher classification accuracy and the average correct classification rate is 96.69%. It proves this texture classification method is reasonable and effective.

The functional model and architecture of collaborative design system for automatic weapon were analyzed. A unified module template instance and its information access relationship model of automatic weapon were presented to achieve practical collaborative design. The proposed system includes configuration management, model-lib management, integrated service model, hierarchical collaborative design process model and cooperative behavior prediction and analysis module which assist collaborative design of modularized products. Combining the API functions development technology of CAD software and the supports of Web services technology with common language interface, the system has been implemented in reliable distributed interactive supporting environment.