Download PDF by Guang-Zhong Yang (auth.), Thanaruk Theeramunkong, Boonserm: Advances in Knowledge Discovery and Data Mining: 13th

Download PDF by Guang-Zhong Yang (auth.), Thanaruk Theeramunkong, Boonserm: Advances in Knowledge Discovery and Data Mining: 13th

By Guang-Zhong Yang (auth.), Thanaruk Theeramunkong, Boonserm Kijsirikul, Nick Cercone, Tu-Bao Ho (eds.)

ISBN-10: 3642013066

ISBN-13: 9783642013065

ISBN-10: 3642013074

ISBN-13: 9783642013072

This booklet constitutes the refereed court cases of the thirteenth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2009, held in Bangkok, Thailand, in April 2009.

The 39 revised complete papers and seventy three revised brief papers offered including three keynote talks have been conscientiously reviewed and chosen from 338 submissions. The papers current new rules, unique examine effects, and sensible improvement stories from all KDD-related parts together with facts mining, information warehousing, desktop studying, databases, data, wisdom acquisition, computerized medical discovery, info visualization, causal induction, and knowledge-based systems.

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Extra resources for Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009 Proceedings

Sample text

We extend the measures used in tradition decision tree, such as information entropy and information gain, for handling data uncertainty. Our experiments demonstrate that DTU can process both uncertain numerical data and uncertain categorical data. It can achieve satisfactory classfication and prediction accuracy even when data is highly uncertain. References 1. html 2. : On density based transforms for uncertain data mining. In: ICDE, pp. 866–875 (2007) 3. : A survey and critique of techniques for extracting rules from trained artificial neural networks.

1. Local vs. global vs. random approaches (c) comparison 26 X. He et al. Algorithm 4. Choose Representatives using Heuristics (DegreeType, Percentage): a greedy local approach Require: k parties P1 , . . , Pk each holding subgraph Gi (Vi , Ei ) as parts of global graph G(V, E) Require: DegreeType: chosen from inter-degree, local-degree, or combined-degree Require: Percentage: representative rate as opposed to the total number of the inter-nodes (|VI |) At Pi : (i) the representative set RSi ← VI (i) for each node u ∈ VI do if DegreeType = inter-degree then Count the inter-degree DI (u) of u else if DegreeType = local-degree then Count the local-degree DL (u) of u else Count the combined-degree DC (u) of u end if end for Sort RSi in terms of the degree counts Keep Percentage*|VI | of nodes with the highest degree in RSi Each party participates the forming of the matrix R using RSi (Similar to the formation of adjacency matrix A discussed in Section 2) 15: return R 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: combined-degree is slightly better than the inter-degree.

W; 29: end if 30: end for; 31: end if ; 32: for each Di do 33: attach the node returned by DTU(Di , att-list); 34: end for; end 1. The tree starts as a single node representing the training samples (step 1). 2. If the samples are all of the same class; then the node becomes a leaf and is labeled with that class (steps 2 and 3). 3. Otherwise, the algorithm uses a probabilistic entropy-based measure, known as the probabilistic information gain ratio, as the criteria for selecting the attribute that will best separate the samples into an individual class (step 7).

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Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009 Proceedings by Guang-Zhong Yang (auth.), Thanaruk Theeramunkong, Boonserm Kijsirikul, Nick Cercone, Tu-Bao Ho (eds.)


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