Bingru Yang

Index-BitTableFI: An improved algorithm for mining frequent itemsets

Efficient algorithms for mining frequent itemsets are crucial for mining association rules as well as for many other data mining tasks. Methods for mining frequent itemsets have been implemented using a BitTable structure. BitTableFI is such a recently proposed efficient BitTable-based algorithm, which exploits BitTable both horizontally and vertically. Although making use of efficient bit wise operations, BitTableFI still may suffer from the high cost of candidate generation and test. To address this problem, a new algorithm Index-BitTableFI is proposed. Index-BitTableFI also uses BitTable horizontally and vertically. To make use of BitTable horizontally, index array and the corresponding computing method are proposed. By computing the subsume index, those itemsets that co-occurrence with representative item can be identified quickly by using breadth-first search at one time. Then, for the resulting itemsets generated through the index array, depth-first search strategy is used to generate all other frequent itemsets. Thus, the hybrid search is implemented, and the search space is reduced greatly. The advantages of the proposed methods are as follows. On the one hand, the redundant operations on intersection of tidsets and frequency-checking can be avoided greatly; On the other hand, it is proved that frequent itemsets, including representative item and having the same supports as representative item, can be identified directly by connecting the representative item with all the combinations of items in its subsume index. Thus, the cost for processing this kind of itemsets is lowered, and the efficiency is improved. Experimental results show that the proposed algorithm is efficient especially for dense datasets.

Efficient attribute reduction based on discernibility matrix

To reduce the time complexity of attribute reduction algorithm based on discernibility matrix, a simplified decision table is first introduced, and an algorithm with time complexity O(|C||U|) is designed for calculating the simplified decision table. And then, a new measure of the significance of an attribute is defined for reducing the search space of simplified decision table. A recursive algorithm is proposed for computing the attribute significance that its time complexity is of O(|U/C|) . Finally, an efficient attribute reduction algorithm is developed based on the attribute significance. This algorithm is equal to existing algorithms in performance and its time complexity is O(|C||U|) + O(|C|2|U/C|).

KDK BASED DOUBLE-BASIS FUSION MECHANISM AND ITS PROCESS MODEL

Knowledge Discovery in Knowledge Base (KDK) opens new horizons for research. KDK and KDD (Knowledge Discovery in Database) are the different cognitive field and discovery process. In most peoples view, they are independent each other. In this paper we can summarize the following tasks: Firstly, we discussed that two kinds of the process model and mining algorithm of KDK based on facts and rules in knowledge base. Secondly, we proves that the inherent relation between KDD and KDK (i.e. double-basis fusion mechanism). Thirdly, we gained the new process model and implementation technology of KDK*. Finally, the imitation experimentation proved that the validity of above mechanism and process model.

INDEX-MAXMINER: A NEW MAXIMAL FREQUENT ITEMSET MINING ALGORITHM

Because of the inherent computational complexity, mining the complete frequent item-set in dense datasets remains to be a challenging task. Mining Maximal Frequent Item-set (MFI) is an alternative ...

Intelligent Decision Support System Based on Data Mining: Foreign Trading Case Study

In this paper, we propose an intelligent decision support system based on data mining (IDSSDM), which integrates several data mining techniques and considers both structured data and semi-structured data. For structured transactional data, online analytical processing (OLAP) is first used to access data warehouse for multidimensional analysis and primary decision support. To uncover hidden relationships among different attributes, KDD*, a software designed by us, is used for discovering association rules among massive trading data. As for semi-structured data, classification and clustering is exploited for contract documents mining; while Web usage mining is used for analyzing the behavior of the users in order to extract relationships in the recorded data. Furthermore, knowledge discovery in knowledge base (KDK) is used as the primary inference engine. As the main business intelligence tool, the system has been adopted by E-Commerce Center of Ministry of Commerce of the Peoples Republic of China.

Generalized Causal Inductive Reasoning Model Based on Generalized Causal Cellular Automata

Based on cellular automata theory, inductive logic causal model is proposed. Then the generalized causal cellular automata, which can deal with both random uncertainty and fuzzy uncertainty, is proposed by integrating the inductive logic causal model and language field theory. Based on the generalized causal cellular automata, the generalized causal inductive reasoning model is presented. Comparisons with ordinary fuzzy reasoning are listed. Finally, the applications of proposed methods are discussed in brief

Research and Realization of Trusted Computing Platform Based on EFI

Trusted computing has become a new and challenging research issue in the field of information security. To further enhance the safety of BIOS, we construct a trusted computing platform based on Extensible Firmware Interface (EFI), the trust chain is transferred from the first stage of EFI to the operating system by applying TPM (Trusted Platform Module) and TSS (TCG Software Stack). We presented the principle mechanism of EFI and the realization framework of trusted computing platform, proposed the key technologies such as Chain of Trust, construction of TMP and TSS, validation of file integrity, and etc. Theoretic analysis demonstrated the effectiveness of this new trusted computing platform.

Study of cost-sensitive ant colony data mining algorithm

Recently, cost-sensitive data mining has been an area of extensive research interests. Intelligent ant colony classification algorithm is introduced in cost-sensitive data mining method in order to obtain satisfied classification results by interaction of ant individuals. The Convergence rate of classification is increased by using of MetaCosts Meta-learning theory. Moreover, Boosting theory is investigated to improve the classification results. As a result, some satisfied performances can be obtained by the combination of above three theories.

Preserving Private Knowledge In Decision Tree Learning

Data mining over multiple data sources has become an important practical problem with applications in different areas. Although the data sources are willing to mine the union of their data, they don’t want to reveal any sensitive and private information to other sources due to competition or legal concerns. In this paper, we consider two scenarios where data are vertically or horizontally partitioned over more than two parties. We focus on the classification problem, and present novel privacy preserving decision tree learning methods. Theoretical analysis and experiment results show that these methods can provide good capability of privacy preserving, accuracy and efficiency.

A New Scheme on Privacy-Preserving Distributed Decision-Tree Mining

Privacy-preserving data mining is discovering accurate patterns and rules without precise access to the original data. This paper focuses on privacy-preserving research in the situation of distributed decision-tree mining, and presents a decision-tree mining algorithm based on homomorphic encryption technology, which can get accurate mining effect in the premise of no sharing of private information among mining participators. Theoretical analysis and experiment results show that this algorithm can provide good capability of privacy-preserving, accuracy and efficiency.