This paper proposes Particle Swarm Optimization (PSO) algorithm to discover classification rules. The potential IF-THEN rules are encoded into real-valued particles that contain all types of attributes in data sets. Rule discovery task is formulized into an optimization problem with the objective to get the high accuracy, generalization performance, and comprehensibility, and then PSO algorithm is employed to resolveit. The advantage of the proposed approach is that it can be applied on both categorical data and continuous data. The experiments are conducted on two benchmark data sets: Zoo data set, in which all attributes are categorical, and Wine data set, in which all attributes except for the classification attribute are continuous. The results show that there is on average the small number of conditions per rule and a few rules per rule set, and also show that the rules have good performance of predictive accuracy and generalization ability.

Since 3D models have been widely applied in many research areas, the techniques for content-based 3D model retrieval become necessary. In this paper, a novel visual based 3D shape descriptor called MATE is proposed. A modi-ed Principal component analysis (PCA) method for model normalization is presented at-rst. Sec-ondly, a new Adjacent angle distance Fourier (AADF) al-gorithm is proposed. Then the original two-viewed Dbucrer method is presented to extract characteristics of projected images. Finally, based on the modi-ed PCA method, the shape descriptor MATE is proposed by combining AADF, Tchebichef and two-viewed Dbu®er. Experimental results show that the descriptor MATE provides better retrieval performance than the best current descriptors.

Due to the widely application of 3D models, the techniques of content-based 3D shape retrieval become necessary. In this paper, a modified Principal Component Analysis (PCA) method for model normalization is introduced at first, and each model is projected in 6 different viewpoints. Secondly, a new adjacent angle distance Fouriers (AADF) descriptor is presented, which captures more precise contour feature of black-white images. Finally, based on modified PCA method, a novel composite 3D shape descriptor MADE is proposed by concatenating AADF, Tchebichef and D-buffer descriptors. Experimental results on the criterion of 3D model database PSB show that the proposed descriptor MADE has gained the best retrieval effectiveness compared with three single descriptors and two composite descriptors LFD and DESIRE.

As the need for personalized services sharply increases caused by the booming of Internet, Web-based data-mining is becoming a valuable sources of thoughts and theory to satisfy the personalized system function. The characters of personalized data-mining is reviewed and discussed in the beginning, and then an innovative algorithm (FP-Tree time-validity algorithm) of Web pageviews, based on personalization, is raised. More authentic information can be efficiently got by adding time-validity coefficient to FTT-Tree storage structure to implement increment mining.

This paper proposes a general formalism for evaluating hybrid Bayesian networks. The formalism approximates a hybrid Bayesian network into the form, called fuzzy partial least-squares Bayesian network (FPBN). The form replaces each continuous variable whose descendants include discrete variables by a partner discrete variable and adding a directed link from that partner discrete variable to the continuous one. The partner discrete variable is acquired by the discretization of the original continuous variable with a fuzzification algorithm based on the structure adaptive-tuning neural network model. In addition, the dependence between the partner discrete variable and the original continuous variable is approximated by fuzzy sets, and the dependence between a continuous variable and its continuous and discrete parents is approximated by a conditional Gaussian regression (CGR) distribution in which partial least-squares (PLS) is proposed as an alternative method for computing the vector of regression parameter. The experimental results are included to demonstrate the performances of the new approach.