Download Intelligent Information Systems 2001: Proceedings of the by Kwei Aryeetey, Wojciech Ziarko, Patricia Tai, Casely Ago PDF

By Kwei Aryeetey, Wojciech Ziarko, Patricia Tai, Casely Ago (auth.), Assoc. Prof. Dr.-Eng. Mieczysław A. Kłopotek, Dr.-Eng. Maciej Michalewicz, Assoc. Prof. Dr.-Eng. Sławomir T. Wierzchoń (eds.)

The ebook bargains a range of papers offered on the overseas symposium Intelligent info platforms X held in Zakopane, Poland. The papers record on growth in conception and purposes of largely understood synthetic intelligence, together with desktop studying, wisdom discovery, wisdom established structures and reasoning, clever statistical research and delicate computing (i.e. fuzzy and tough units, neural networks, evolutionary algorithms and synthetic immune systems). fascinating new theoretical effects are awarded and their functional applicability confirmed. the amount additionally indicates tough new examine issues.

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Extra resources for Intelligent Information Systems 2001: Proceedings of the International Symposium “Intelligent Information Systems X”, June 18–22, 2001, Zakopane, Poland

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A more flexible way of implementing epsilon would be to adjust it dynamically, so that some criterion related to the description quality is optimized . • • • • • • • I • Fig 1,2,3 Rule generalization for different values of epsilon Figures 1, 2 and 3 illustrate rule generation with different epsilon values. The number of attributes is two, so the representation space is a plane. The five examples are represented by dots . In each figure , the central dot represents the seed, and the other four dots represent negative examples.

In practice miners are interested in finding dependencies consist of selected feature because discovering all rules in database is very complex task. The advantage of our method is this that we generate only most characteristic dependencies for chosen attributes. Moreover, there are many interesting and useful dependencies with low support but with high confidence. Known algorithms need a lot of time to discover association rules with low support. Proposed methodology allows discover either strong or weak relationships without additional computation.

8) & (Asymmetry, one_axiaLsymmetry) & (Color_white, 0) & (Border, 2 . 6) . 9 .. 9 .. 9 .. 9 .. 9 .. 9.. 8) & (Diversity-structureless_areas, 0) -> (Melanoma, suspicious) 48 (Border, 6 .. 8) & (Coloc white, 1) & (Diversity-pigmencdots, 0) & (Diversity_pigmencnetwork, 0) -> (Melanoma, suspicious) (Border, 6 . 8) & (Color_black, 0) & (Diversity_branched_stricks, 1) & (Asymmetry, one_axiaLsymmetry) & (Diversity_pigment_network, 1) & (Di versity _pigmenCdots, 1) & (Di versity _structureless_areas, 1) & (Diversity-pigmenCglobules, 1) -> (Melanoma, suspicious) Every rule is preceded by its strength.

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