By Lotfi A. Zadeh (auth.), Professor Tsau Young Lin, Professor Yiyu Y. Yao, Professor Lotfi A. Zadeh (eds.)
During the earlier few years, facts mining has grown swiftly in visibility and significance inside of info processing and determination research. this is often par ticularly real within the realm of e-commerce, the place info mining is relocating from a "nice-to-have" to a "must-have" prestige. In a distinct notwithstanding comparable context, a brand new computing method known as granular computing is rising as a strong software for the belief, research and layout of information/intelligent structures. In essence, info mining bargains with summarization of knowledge that is resident in huge info units, whereas granular computing performs a key position within the summarization strategy by means of draw ing jointly issues (objects) that are comparable via similarity, proximity or performance. during this standpoint, granular computing has a place of centrality in information mining. one other method which has excessive relevance to info mining and performs a vital position during this quantity is that of tough set conception. essentially, tough set concept might be seen as a department of granular computing. despite the fact that, its purposes to information mining have predated that of granular computing.
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Additional resources for Data Mining, Rough Sets and Granular Computing
11. 1 and 2 has C(16,3) and C( 48,3) subsets respectively. Finding 3-largecombinations with Apriori is better than AprioriSubset. AprioriHybrid needs the 3-candidate bar (Ci in aprioriTid) tables when finding 4-large-combinations. So the 3-candidate bar table is created while finding 3-large-combinations. As a result, the execution on finding 3-largecombinations in AprioriHybrid is longer than AprioriSubset. On data 1, the table and granule sizes are smaller than the memory size. Finding large-combinations for data 1 is memory-based.
Imielinski, and A. Swami, "Mining Association Rules Between Sets of Items in Large Databases," in Proceeding of ACM-SIGMOD International Conference on Management of Data, pp. 207-216, Washington, DC, June, 1993. 2. , R. Srikant, "Fast Algorithms for Mining Association Rules," in Proceeding of 20th VLDB Conference San Tiago, Chile, 1994. 3. W. Chu and Q. Chen, "Neighborhood and associative query answering," Journal of Intelligent Information Systems, vol 1, 355-382, 1992. 4. , Addison-Wesley, 2000.
They will be denoted by EA nEB-granules or (EA, EB)-granules. This results are, in fact, valid for any number of equivalence relations. 4. The collection of all elementary granules constitutes a set, called the quotient set and denoted by VIEA or simply VIA. 5. An elementary granule plays two roles: one as an element of the quotient set V I A, another as a subset of the universe V. We can regard the element as the canonical name or label of the subset. In other words, the quotient set is a set that consists of all canonical names.