By Hosagrahar Visvesvaraya Jagadish⋆ (auth.), David Cheung, Graham J. Williams, Qing Li (eds.)
This booklet constitutes the refereed lawsuits of the fifth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2001, held in Hong Kong, China in April 2001.
The 38 revised complete papers and 22 brief papers offered have been rigorously reviewed and chosen from a complete of 152 submissions. The booklet bargains topical sections on net mining, textual content mining, purposes and instruments, inspiration hierarchies, function choice, interestingness, series mining, spatial and temporal mining, organization mining, category and rule induction, clustering, and complicated subject matters and new methods.
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Additional resources for Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings
16]) explicitly includes θ in a quadratic formulation that can be transformed to: ˆ = arg inf 1 (w, ˆ θ) w,θ n n g(yi (wT xi − θ)) + λw2 , (3) i=1 where 1 − z if z ≤ 1, 0 if z > 1. g(z) = (4) It is interesting to compare the least squares approach and the support vector machine approach. In the least squares formulation, the loss function (z − 1)2 implies that we try to find a weight w such that wT x ≈ 1 for an in-class data point x, and wT x ≈ −1 for an out-of-class data point x. Although this means that the formulation attempts to separate the in-class data from the out-ofclass data, it also penalizes a well behaved data point x such that wT xy > 1.
From the implementation point of view, we will demonstrate one of the most important applications of iJADE in the ecommerce environment - iJADE eMiner. iJADE eMiner is a truly intelligent agentbased Web-mining application which consists of two major modules: 1) an agentbased facial pattern authentication scheme - FAgent, an innovative visual data mining and visualization scheme using EGDLM technique ; 2) a Web-mining tool based on fuzzy shopping agents for product selection and brokering - FShopper.
2 depicts the two level abstraction in the iJADE system: a) iJADE system level - ACTS model, and b) iJADE data level - DNA model. The ACTS model consists of 1) the Application Layer, 2) the Conscious (Intelligent) Layer, 3) the Technology Layer, and the 4) Supporting Layer. The DNA model is composed of 1) the Data Layer, 2) the Neural Network Layer, and 3) the Application Layer. Compared with contemporary agent systems which provide minimal and elementary data management schemes, the iJADE DNA model provides a comprehensive data manipulation framework based on neural network technology.
Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings by Hosagrahar Visvesvaraya Jagadish⋆ (auth.), David Cheung, Graham J. Williams, Qing Li (eds.)