S. Sahar. Interestingness via what is not interesting. In S. Chaudhuri and D. Madigan, editors, Fifth International Conference on Knowledge Discovery and Data Mining, pages 332--336, San Diego, CA, USA, 1999. ACM Press.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....the number of patterns rules discovered under the support model can be very large. Many post processing techniques have been developed to reduce the number of discovered patterns into a manageable size while preserving the discovered knowledge as much as possible. Human interaction is involved in [12, 22, 23] to specify the interestingness or beliefs to guide the process while others [13, 14] focused on reducing redundant information possessed by the discovered rules. It is clear that these post processing techniques are typically used as an additional pruning step after the normal mining procedure ....

S. Sahar. Interestingness via what is not interesting. Proc. 5th ACM Int. Conf. on Knowledge Discovery and Data Mining (SIGKDD), pp. 332-336, 1999.

....constraints that select only certain types of rules from the set of all the discovered rules; examples of this research include [46, 53, 55] In these approaches the user specifies constraints but does not do it iteratively. In contrast to this, it has been observed by several researchers, e.g. [18, 32, 72, 67, 50, 2, 69], that knowledge discovery should be an iterative and interactive process that involves an explicit participation of the domain expert. In our research we have followed the latter approach and applied it to the rule validation process. Note, that the quality of discovered rules can be defined ....

....algorithm would have to be reexecuted with the correct constraint, which is more computationally expensive than to reexecute a correct filtering operator in the post analysis phase. The benefits of iterative analysis of data mining results are also pointed out by several researchers, including [32, 72, 67, 50, 69]. Therefore, neither post analysis nor the pre specification of constraints works best as a stand alone method, and the two approaches should be combined into one integral method. The main question pertaining to this combination is what kinds of constraints should be pre specified by the user for ....

S. Sahar. Interestingness via what is not interesting. In Proceedings of the Fifth A CM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 1999.

....the set of all the discovered rules; examples of this research include [KMR 94, LH96, LHM99] In these approaches the user specifies constraints but does not do it iteratively. In contrast to this, it has been observed by several researchers, e.g. BA96, FPSS96, ST96a, PJ98, LBA98, AT99, 3 Sah99] that knowledge discovery should be an iterative process that involves an explicit participation of the domain expert, and we apply this point of view to the rule validation process. The rest of the paper is organized as follows. In Section 2, we present our approach to profiles and profile ....

....experiments to prove this point. 27 which is more computationally expensive than to reexecute a correct filtering operator in the post analysis phase. The benefits of iterative analysis of data mining results are also pointed out by several researchers, including [FPSS96, ST96a, PJ98, LBA98, Sah99] Therefore, neither the post analysis nor the pre specification of constraints works best as a stand alone method, and the two approaches should be combined into one integral method. The main question pertaining to this combination is what kinds of constraints should be prespecified by the user ....

S. Sahar. Interestingness via what is not interesting. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 1999.

....interesting results. To address this problem, various techniques have been suggested to reduce and or order the patterns prior to presenting them to the user. For example, in [3] it is shown that the most interesting rules may reside along a support confidence border. A technique is described in [20] that discovers interesting rules via an interactive process that seeks to classify rules that are not interesting. In [8] a measure is described that determines the interestingness (called surprise there) of discovered knowledge via the explicit detection of Simpson s Paradox. An approach is ....

S. Sahar. Interestingness via what is not interesting. In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (KDD'99), pages 332--336, San Diego, California, August 1999.

....the importance of a rule by considering its unexpectedness in terms of other rules in its neighborhood. Another technique is described in [3] that shows the most interesting rules reside along a support confidence border. Looking at the problem from another perspective, a method is described in [22] that attempts to discover interesting rules via an interactive process that seeks to classify rules that are not interesting. And in previous work, we introduced and described the use of diversity measures for ranking the interestingness of summaries generated from databases [11, 12, 16] Also, a ....

S. Sahar. Interestingness via what is not interesting. In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (KDD'99), pages 332--336, San Diego, California, August 1999.

....structure of relevant rules and constrain the search space. Another notable attempt in using objective measures was byBayardo and Agrawal [1] who de ned a partial order, in terms of both support and con dence, to identify a smaller set of rules that were more interesting than the rest. Sahar [24] proposed an iterative elimination of uninteresting rules, limiting user interaction to a few simple classi cation questions. Hussain et al. 10] developed a method for identifying exception rules, with the interestingness of a rule being estimated relative to common sense rules and reference ....

S. Sahar. Interestingness via what is not interesting. In ### ##### ### ###### ############# ########## ## ######### ######### ### #### ######, pages 332-336, August 1999.

....of them have no or almost no occurrence in the negative data. Ranking discovered patterns is an intensively studied topic in data mining, the readers are referred to (Klemettinen et al. 1994; Silberschatz Tuzhilin, 1996; Dong Li, 1998; Padmanabhan Tuzhilin, 1998; Bayardo Agrawal, 1999; Sahar, 1999; Hilderman Hamilton, 2001) for other subjective and objective measurements originated in information theory, statistics, ecology, and economics. 8 Performance Evaluation: Accuracy, Speed, and Scalability We report in this section the performance of our method in comparison to the performance ....

Sahar, S. (1999). Interestingness via what is not interesting. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 332--336). San Diego, CA: ACM Press.

....structure of relevant rules and constrain the search space. Another notable attempt in using objective measures was by Bayardo and Agrawal [1] who de ned a partial order, in terms of both support and con dence, to identify a smaller set of rules that were more interesting than the rest. Sahar [24] proposed an iterative elimination of uninteresting rules, limiting user interaction to a few simple classi cation questions. Hussain et al. 10] developed a method for identifying exception rules, with the interestingness of a rule being estimated relative to common sense rules and reference ....

S. Sahar. Interestingness via what is not interesting. In The Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 332-336, August 1999.

.... that while support and confidence were introduced in (Agrawal, Imielinski and Swami 1993) considerable research has been undertaken into the nature of interestingness in mining rules see for example (Silberschatz and Tuzhilin 1996; Dong and Li 1998; Bayardo Jr and Agrawal 1999; Freitas 1999; Sahar 1999). 3 form of rule require the use of spatial and temporal predicates (Koperski and Han 1995; EstivillCastro and Murray 1998) Moreover, it should be noted that for temporal association rules, the emphasis moves from the data itself to changes in the data (Chen, Petrounias and Heathfield 1998; Ye ....

SAHAR, S. (1999): Interestingness via what is not interesting. Proc. Fifth International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 332-336, CHAUDHURI, S. and MADIGAN, D. (eds). ACM Press.

....structure of relevant rules and constrain the search space. Another notable attempt in using objective measures was by Bayardo and Agrawal [BA99] who de ned a partial order, in terms of both support and con dence, to identify a smaller set of rules that were more interesting than the rest. Sahar [Sah99] proposed an iterative elimination of uninteresting rules, limiting user interaction to a few simple classi cation questions. Hussain et al. HLSL00] developed a method for identifying exception rules, with the interestingness of a rule being estimated relative to common sense rules and reference ....

Sigal Sahar. Interestingness via what is not interesting. In The Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 332-336, August 1999.

....for ranking discovered knowledge. Numerous techniques for determining the interestingness of discovered knowledge have recently been reported in the literature. For example, in In [3] it is shown that the most interesting rules reside along a support confidence border. A technique is described in [18] that discovers interesting rules by eliminating those that are not interesting. And an extensive survey of recently proposed interestingness measures is described in [9] In this paper, we investigate the problem of ranking the interestingness of summaries generated from databases, where a single ....

S. Sahar. Interestingness via what is not interesting. In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (KDD'99), pages 332--336, San Diego, California, August 1999.

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S. Sahar. Interestingness via what is not interesting. In S. Chaudhuri and D. Madigan, editors, Fifth International Conference on Knowledge Discovery and Data Mining, pages 332--336, San Diego, CA, USA, 1999. ACM Press.

No context found.

S. Sahar. Interestingness via what is not interesting. In S. Chaudhuri and D. Madigan, editors, ##### ######## ###### ########## ## ######### ######### ### #### ######, pages 332-336, San Diego, CA, USA, 1999. ACM Press.

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S. Sahar, \Interestingness via what is not interesting," in Fifth International Conference on Knowledge Discovery and Data Mining (S. Chaudhuri and D. Madigan, eds.), (San Diego, CA, USA), pp. 332-336, ACM Press, 1999.

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