S-rough Sets And Data Sieve-filtration

Since LSI (Large-scale integration) and SLSI (super-large-scale integration) have been produced from 1970s, computers have been an indispensable implement in modern industries, business and agriculture. However, with data expanding rapidly at the same time, the mankind enters a data explosive period very soon. These data have the characteristic of hugeness, random and uncertainty, whose generating process has dynamic characteristic. In fact, it is only a small part needed by people in the large-scale and complicated data which includes abundant information, then how to mine out the information needed becomes an important question for discussion. Rough sets theory was proposed firstly by a Polish mathematician Z.Pawlak in 1982, which is a new mathematic implement used to solve problems about incompletion and imprecision, and it gets the knowledge by data reduction using equivalent relation and approximation concept. Rough sets knowledge system is based on rule, which is a conclusion about experience and needn't the exact mathematic description, so it meets the intuitionistic, simple, comprehensible, humanized and intelligentized demands in the data disposal process, providing the theory base and research direction for data mining technique.The traditional data mining method is discussed under the assumption that the data won't change, which can be called a static data mining method. But actually data can't keep unaltered, so when the data changes, the static data mining method won't do its work, which is one limitation of the traditional data mining method. S-rough sets (Singular Rough Sets) which is proposed by Professor Shi Kaiquan with Shandong University in 2002, is an improvement based on Z.Pawlak rough sets, and it is a dynamic rough sets based on element transfer. S-rough sets has three forms: one direction S-rough sets (One direction Singular rough sets), dual of one direction S-rough sets (Dual of one direction Singular rough sets), and two direction S-rough sets (Two direction Singular rough sets). S-rough sets has dynamic, hereditary and granularity characteristic, which proposes a new research direction and provides the theory bases for dynamic data mining.The main work in this paper is as following:1. Introducing the research actuality of the data mining and its classes, and explaining the background, development, the research content and the direction of Rough sets theory, and outlining S-rough sets theory.2. By using dynamic, hereditary and granularity characteristic of S-rough sets, this paper carries the research of S-rough sets and data filter-filtration which includes discussing the granularity characteristic, one direction filter-filtration and two direction filter- filtration of data, and giving the concepts of f-filter-filtration degree, f-filter-filtration degree and (?)-filter-filtration degree, and proposing filter-filtration theorem and filter-filtration criterion.3. Proposing a kind of dynamic clustering algorithm based on S-rough sets with the results in chapter 3, which improves the clustering algorithm of WSN (wireless sensor net). Comparing it with the existing algorithms, we make such a conclusion: the algorithm makes the energy used equably at every node and the ratio of energy consumption to energy available reduced, so it meets the actual requirement for WSN.