TAILIEUCHUNG - Improve efficiency of fuzzy association rule using hedge algebra approach

This paper proposes a method for mining fuzzy association rules using compressed database. We also use the approach of Hedge Algebra (HA) to build the membership function for attributes instead of using the normal way of fuzzy set theory. This approach allows us to explore fuzzy association rules through a relatively simple algorithm which is faster in terms of time, but it still brings association rules which are as good as the classical algorithms for mining association rules. | Journal of Computer Science and Cybernetics, , (2014), 397–408 DOI: IMPROVE EFFICIENCY OF FUZZY ASSOCIATION RULE USING HEDGE ALGEBRA APPROACH TRAN THAI SON1 , NGUYEN TUAN ANH2 1 Institute of Information Technology, Vietnam Academy of Science and Technology; trn˙thaison@ 2 University of Information and Communication Technology, Thai Nguyen University; anhnt@ Abstract. A major problem when conducting mining fuzzy association rules from the database (DB) is the large computation time and memory needed. In addition, the selection of fuzzy sets for each attribute of the database is very important because it will affect the quality of the mining rule. This paper proposes a method for mining fuzzy association rules using compressed database. We also use the approach of Hedge Algebra (HA) to build the membership function for attributes instead of using the normal way of fuzzy set theory. This approach allows us to explore fuzzy association rules through a relatively simple algorithm which is faster in terms of time, but it still brings association rules which are as good as the classical algorithms for mining association rules. Keywords. Data mining, association rules, compressed transactions, knowledge discovery, hedge algebras 1. INTRODUCTION In recent years, the fast development of technologies has made the collecting and storing abilities of information systems quickly increase. Moreover, the computerization of the production, sales and many other activities has created a huge amount of data needed for storage. There have been so many very large databases among millions of records used in the aforementioned activities. This boom has led to an urgent demand that is necessary to apply new techniques and tools in order to extract huge amounts of data to useful knowledge. Therefore, data mining techniques have attracted a great deal of attention in the field of information technology. Mining association rules have

• Kỹ thuật lập trình
• Data mining
• Association rules
• Compressed transactions
• Knowledge discovery
• Hedge algebras
• Data warehousing and data mining
• Data warehousing
• Data warehouse modelling
• Data mining functionalities
• Association rule mining
• FP growth algorithm
• K Nearest neighbor classification
• Lecture Data mining
• Bài giảng Khai thác dữ liệu
• Data mining for data streams
• Mining data streams
• Stream frequent pattern analysis
• Khai phá dữ liệu
• Overview of data mining
• Other Data Mining problems
• Applications of data mining
• Sequence data
• Sequential pattern mining
• Sequential pattern mining algorithms
• Data stream mining
• Data stream processing algorithms
• Cash register model
• Time series model
• Lecture Data mining and analysis
• Khai thác dữ liệu
• Data analysis foundations
• Frequent pattern mining
• Itemset mining
• Sequence mining
• Graph pattern mining
• Data warehouses
• On line analytical processing
• Association rules mining
• Mining association rules
• Computational complexity
• Frequent itemset generation
• Mining frequent closed patterns
• Data warehouse
• Data warehouse architecture
• Data warehouse implementation
• Data warehouse—subject oriented
• Graph data
• High dimensional data
• Numeric attributes
• Categorical attributes
• Kernel methods
• Dimensionality reduction
• Summarizing itemsets
• Rule assessment