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We discuss information granule calculi as a basis of granular computing. They are defined by constructs like information granules, basic relations of inclusion and closeness between information granules as well as operations on them. The exact interpretation between granule languages of different information sources (agents) often does not exist. Hence (rough) inclusion and closeness of granules are considered instead of their equality. Examples of all the basic constructs of information granule calculi are presented. The construction of more complex information granules is described by expressions called terms. We discuss the synthesis problem of robust terms, i.e., descriptions of information granules, satisfying a given specification in a satisfactory degree. We also present a method for synthesis of information granules represented by robust terms (approximate schemes of reasoning) by means of decomposition of specifications for such granules. The discussed problems of granular computing are of special importance for many applications, in particular related to spatial reasoning as well as to knowledge discovery and data mining.

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The tumor suppressor gene CDKN2 (p16/MTS1) resides on chromosome 9p21 and encodes a 16 kDa inhibitor of the cyclin-dependent kinases. Inactivation of CDKN2 by homozygous deletion, point mutation, and recently described aberrant methylation in the 5' promoter region may increase progression through the cell cycle in tumors. In this study, we examine the CDKN2 gene for the presence of inactivating alterations in human prostate cancer. Sequence analysis of cell lines revealed no mutation in LNCaP, PC3, and TSU-PR1 and a missense mutation, GAC-->TAC (asp to tyr), in exon 2 of the DU145 cell line at codon 76. No mutations were identified in three primary prostate cancers or in seven lymph node metastases. Loss of heterozygosity (LOH) was analyzed by analysis of microsatellite markers in the vicinity of the CDKN2 gene. LOH was detected in 12 (20%) of 60 primary tumors at one or more loci and in 13 (46%) of 28 metastases. Methylation analysis of the CpG-rich promoter region revealed a dense methylation of CDKN2 in cell lines PC3, PPC1, and TSU-PR1, and this was found to correlate with a lack of mRNA expression by reverse transcription-polymerase chain reaction. A demethylating agent, 5-aza-2'-deoxycytidine, induced reexpression when cells were exposed in vitro. DU145 and LNCaP expressed the CDKN2 transcript and were unmethylated in the promoter region. Three of twenty-four (13%) primary prostate cancers and 1 of 12 metastatic tumors demonstrated promoter methylation. No normal prostate tissues were methylated at the CDKN2 gene promoter. One tumor was found to contain concomitant LOH and promoter methylation indicative of biallelic inactivation. A comprehensive analysis of CDKN2 in prostate cancer reveals that point mutations are infrequent, but gene deletion and methylation combine to inactivate CDKN2 in a subset of tumors. Moreover, alterations in this gene may represent a late event in prostate cancer progression.

Extended AbstractTwo most popular approaches to facilitate searching for information on the web are represented by web search engine and web directories. Although the performance of search engines is improving every day, searching on the web can be a tedious and time-consuming task due to the huge size and highly dynamic nature of the web. Moreover, the user's "intention behind the search" is not clearly expressed which results in too general, short queries. Results returned by search engine can count from hundreds to hundreds of thousands of documents.One approach to manage the large number of results is clustering. Search results clustering can be defined as a process of automatical grouping search results into to thematic groups. However, in contrast to traditional document clustering, clustering of search results are done on-the-fly (per user query request) and locally on a limited set of results return from the search engine. Clustering of search results can help user navigate through large set of documents more efficiently. By providing concise, accurate description of clusters, it lets user localizes interesting document faster.In this paper, we proposed an approach to search results clustering based on Tolerance Rough Set following the work on document clustering [4,3]. Tolerance classes are used to approximate concepts existed in documents. The application of Tolerance Rough Set model in document clustering was proposed as a way to enrich document and cluster representation with the hope of increasing clustering performance.Tolerance Rough Set Model: (TRSM) was developed in [3] as basis to model documents and terms in information retrieval, text mining, etc. With its ability to deal with vagueness and fuzziness, TRSM seems to be promising tool to model relations between terms and documents. In many information retrieval problems, defining the similarity relation between document-document, term-term or termdocument is essential.Let D = {d 1 , . . . , d N } be a set of documents and T = {t 1 , . . . , t M } set of index terms for D. TRSM is an approximation space (see [5]) R = (T, I θ , ν, P ) determined over the set of terms T (universe of R) as follows:

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Abstract. We present a general encoding scheme for a wide class of problems (including among others such problems like data reduction, feature selection, feature extraction, decision rules generation, pattern extraction from data or conflict resolution in multi-agent systems) and we show how to combine it with a propositional (Boolean) reasoning to develop efficient heuristics searching for (approximate) solutions of these problems. We illustrate our approach by examples, we show some experimental results and compare them with those reported in literature. We also show that association rule generation is strongly related with reduct approximation.