Association Rule Mining for Multiple Tables With Fuzzy Taxonomic Structures
نویسندگان
چکیده
منابع مشابه
Fuzzy Association Rule Mining
Corresponding Author: Lekha. A., Research Scholar, Dr M G R Educational Research Institute, Chennai, India-600095, Assistant Professor, Department of MCA, PESIT, Bangalore Email: [email protected] Abstract: The paper attempts to propose a fuzzy logic association algorithm to predict the risks involved in identifying diseases like breast cancer. Fuzzy logic algorithm is used to find associatio...
متن کاملA Mining Algorithm under Fuzzy Taxonomic Structures
Most conventional data-mining algorithms identify the relationships among transactions using binary values and find rules at a single concept level. Transactions with quantitative values and items with taxonomic relations are, however, commonly seen in real-world applications. Besides, the taxonomic structures may also be represented in a fuzzy way. This paper thus proposes a fuzzy multiple-lev...
متن کاملOptimizing Membership Functions using Learning Automata for Fuzzy Association Rule Mining
The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since t...
متن کاملMining fuzzy generalized association rules from quantitative data under fuzzy taxonomic structures
Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions has become an important research area. Most conventional data-mining algorithms identify the relationships among transactions using binary values and find rules at a single concept level. Transactions with quantitative values and items with taxonomic relations...
متن کاملFuzzy Logic -based Pre-processing for Fuzzy Association Rule Mining
Conventional Association Rule Mining (ARM) algorithms usually deal with datasets with categorical values and expect any numerical values to be converted to categorical ones using ranges (Age = 25 to 60). Fuzzy logic is used to convert quantitative values of attributes to categorical ones so as to eliminate any loss of information arising due to sharp partitioning (using ranges) and then generat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Theory and Engineering
سال: 2010
ISSN: 1793-8201
DOI: 10.7763/ijcte.2010.v2.253