Data Mining, Rough Sets and Granular Computing by Lotfi A. Zadeh (auth.), Professor Tsau Young Lin, Professor

By Lotfi A. Zadeh (auth.), Professor Tsau Young Lin, Professor Yiyu Y. Yao, Professor Lotfi A. Zadeh (eds.)

During the prior few years, facts mining has grown speedily in visibility and significance inside details 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 special even though comparable context, a brand new computing method referred to as granular computing is rising as a strong instrument for the notion, research and layout of information/intelligent platforms. In essence, facts mining bargains with summarization of data that is resident in huge information units, whereas granular computing performs a key function within the summarization procedure 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 technique which has excessive relevance to information mining and performs a principal function during this quantity is that of tough set concept. essentially, tough set conception should be seen as a department of granular computing. even though, its purposes to information mining have predated that of granular computing.

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So the total number of pages is equal to 13* (1654* 1 +446298*2+ 1807 *3+ 10*4). The use of granules offers a promising method to find association rules. The AND, SHIFTS, and ADD operations among granules are natural for general machine architecture, thus making the operations fast. The disadvantage with granules is the costs of creating the temporary table to hold the granules. However, the gain is that the data is converted to a compact form, thus benefiting the whole process. 7 Conclusions Granular computing approach is faster than Apriori mainly because the "database scan" are replaced by bit operations.

We can regard the attribute A (column) as a projection that maps entities to elementary concepts. Formally, it is the map:(see Appendix 1) p( -, A) : V ----+ Dom(A); v ----+ c = p(v, A)) In Table 2, the attribute STATUS defines the map St: V ----+ Dom(STATUS) 26 St: IDl ----t TWENTY St: ID2 ----t TEN St:ID 3 ----tTEN St: ID4 ----t TWENTY St: ID5 ----t THIRTY The inverse image of the values are: 1. {ID 1,ID4} = SC1(TWENTY) = {v I v E V,St(v) = TWENTY}; 2. {ID 2,ID3 } = SC1(TEN) = {v I v E V,St(v) = TEN}; 3.

The 1st horizontal block consists of the first 4K bits of every granule. The 2nd block consists of the second 4K bits, and etc. The algorithms explained in the last three subsections, apply to each horizontal block, one block at a time; do the first block, then second and etc. We do assume, the main memory is large enough to hold one horizontal block (and make some computation). 6 Experiment Results The experimental result are taken from [8]. Four different data are generated to compare the run time on these algorithms to find association rules; this is an improvement of [9], [10] The data varies the number of tuples, the attributes per relation, and the minimal support.

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