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 swiftly in visibility and significance inside of details processing and selection research. this can be par­ ticularly actual within the realm of e-commerce, the place facts mining is relocating from a "nice-to-have" to a "must-have" prestige. In a special notwithstanding similar context, a brand new computing method known as granular computing is rising as a robust device for the notion, research and layout of information/intelligent platforms. In essence, facts mining offers with summarization of data that is resident in huge info units, whereas granular computing performs a key position within the summarization method by means of draw­ ing jointly issues (objects) that are similar via similarity, proximity or performance. during this viewpoint, granular computing has a place of centrality in facts mining. one other method which has excessive relevance to info mining and performs a primary position during this quantity is that of tough set thought. primarily, tough set concept will be considered as a department of granular computing. in spite of the fact that, its purposes to facts mining have predated that of granular computing.

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Extra resources for Data Mining, Rough Sets and Granular Computing

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In the case of classification problems. the classification of the input data vector (decision CLASSmax associated with the hypothesis (head making) is determined as the class CIASS proposition) with the highest support. A modified decision making procedure based upon utility theory could altematively alternatively be used. where the posterior probability SCLASS (hypothesis support) is multiplied by the utility value of the respective hypothesis and then the classification of the input data vector (decision CLASSmax associated with the hypothesis that making) is determined as the class CIASS maximises the resulting expected utility [14].

For each word, it does the count, first, by every 2 bits, next by every 4 bits, by every 8 bits, and finally by every 16 bits. Here is the sample (we use pseudo C syntax). 1. 2 bits count: odd bits are added to the preceding even bits: Let B be the bit pattern and Count32 is a variable holding the "new" bit pattern. Count32 = (B&Ox55555555) + ((B » 1)&Ox55555555); 2. 4 bits count: odd 2-bits are added to even 2-bits (each 2-bit represents the sum of even and odd bits) Count32 = (Count32&Ox33333333) + ((Count32 » 2)&Ox33333333); 3.

It is a subset of entities, so one can be regarded it as an expression of entities. 2. Assume EA and EB are two partitions (equivalence relations). EA is said to be a refinement of EB iff every elementary granule in EA is contained in an elementary granules in EB. In this case, we may also say that EB is depended on EA; EB is coarse than EA; or EA is finer than E B . 3. Assume EA and EB are two equivalence relations. The intersection EA n EB is another equivalence relation whose granules consists of all possible intersections of EA- and EB-granules (equivalence classes).

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