Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I
With the ever-growing power of generating, transmitting, and collecting huge amounts of data, information overloadis nowan imminent problemto mankind. The overwhelming demand for information processing is not just about a better understanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects ofdata and to help peoplemakeinformed,sensible,and better decisions. At present, growing attention has been paid to the study, development, and application of data mining. As a result there is an urgent need for sophisticated techniques and toolsthat can handle new fields of data mining, e. g. , spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The 6th International Conference on Advanced Data Mining and Appli- tions(ADMA2010)aimed to bring together the experts on data mining throu- out the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and different applied disciplines. The conference attracted 361 online submissions from 34 different countries and areas. All full papers were peer reviewed by at least three members of the Program Comm- tee composed of international experts in data mining fields. A total number of 118 papers were accepted for the conference. Amongst them, 63 papers were selected as regular papers and 55 papers were selected as short papers.
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Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I
With the ever-growing power of generating, transmitting, and collecting huge amounts of data, information overloadis nowan imminent problemto mankind. The overwhelming demand for information processing is not just about a better understanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects ofdata and to help peoplemakeinformed,sensible,and better decisions. At present, growing attention has been paid to the study, development, and application of data mining. As a result there is an urgent need for sophisticated techniques and toolsthat can handle new fields of data mining, e. g. , spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The 6th International Conference on Advanced Data Mining and Appli- tions(ADMA2010)aimed to bring together the experts on data mining throu- out the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and different applied disciplines. The conference attracted 361 online submissions from 34 different countries and areas. All full papers were peer reviewed by at least three members of the Program Comm- tee composed of international experts in data mining fields. A total number of 118 papers were accepted for the conference. Amongst them, 63 papers were selected as regular papers and 55 papers were selected as short papers.
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Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I

Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I

Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I

Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I

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Overview

With the ever-growing power of generating, transmitting, and collecting huge amounts of data, information overloadis nowan imminent problemto mankind. The overwhelming demand for information processing is not just about a better understanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects ofdata and to help peoplemakeinformed,sensible,and better decisions. At present, growing attention has been paid to the study, development, and application of data mining. As a result there is an urgent need for sophisticated techniques and toolsthat can handle new fields of data mining, e. g. , spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The 6th International Conference on Advanced Data Mining and Appli- tions(ADMA2010)aimed to bring together the experts on data mining throu- out the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and different applied disciplines. The conference attracted 361 online submissions from 34 different countries and areas. All full papers were peer reviewed by at least three members of the Program Comm- tee composed of international experts in data mining fields. A total number of 118 papers were accepted for the conference. Amongst them, 63 papers were selected as regular papers and 55 papers were selected as short papers.

Product Details

ISBN-13: 9783642173158
Publisher: Springer Berlin Heidelberg
Publication date: 03/10/2011
Series: Lecture Notes in Computer Science , #6440
Edition description: 2010
Pages: 628
Product dimensions: 0.00(w) x 0.00(h) x 0.04(d)

Table of Contents

I Data Mining Foundations

Cost Sensitive Classification in Data Mining Zhenxing Qin Chengqi Zhang Tao Wang Shichao Zhang 1

Web Users Access Paths Clustering Based on Possibilistic and Fuzzy Sets Theory Hong Yu Hu Luo Shuangshuang Chu 12

Discriminative Markov Logic Network Structure Learning Based on Propositionalization and X2-Test Quang-Thang Dinh Matthieu Exbrayat Christel Vrain 24

EWGen: Automatic Generation of Item Weights for Weighted Association Rule Mining Russel Pears Yun Sing Koh Gillian Dobbie 36

Best Clustering Configuration Metrics: Towards Multiagent Based Clustering Santhana Chaimontree Katie Atkinson Frans Coenen 48

On Probabilistic Models for Uncertain Sequential Pattern Mining Muhammad Muzammal Rajeev Raman 60

Cube Based Summaries of Large Association Rule Sets Marie Ndiaye Cheikh T. Diop Arnaud Giacometti Patrick Marcel Arnaud Soulet 73

A Perceptron-Like Linear Supervised Algorithm for Text Classification Anestis Gkanogiannis Theodore Kalamboukis 86

Research on Time Series Forecasting Model Based on Moore Automata Yixiong Chen Zhongfu Wu Zhiguo Li Yixing Zhang 98

A Clustering Algorithm FCM-ACO for Supplier Base Management Weining Liu Lei Jiang 106

Nearest Neighbour Distance Matrix Classification Mohd Shamrie Sainin Rayner Alfred 114

Classification Inductive Rule Learning with Negated Features Stephanie Chua Frans Coenen Grant Malcolm 125

Fast Retrieval of Time Series Using a Multi-resolution Filter with Multiple Reduced Spaces Muhammad Marwan Muhammad Fuad Pierre-François Marteau 137

DHPTID-HYBRID Algorithm: A Hybrid Algorithm for Association Rule Mining Shilpa Sonawani Amrita Mishra 149

An Improved Rough Clustering Using Discernibility Based Initial Seed Computation Djoko Budiyanto Setyohadi Azuraliza Abu Bakar Zulaiha Ali Othman 161

Fixing the Threshold for Effective Detection of Near Duplicate Web Documents in Web Crawling V.A. Narayana P. Premchand A. Govardhan 169

Topic-Constrained Hierarchical Clustering for Document Datasets Ying Zhao 181

Discretization of Time Series Dataset Using Relative Frequency and K-Nearest Neighbor Approach Azuraliza Abu Bakar Almahdi Mohammed Ahmed Abdul Razak Hamdan 193

MSDBSCAN: Multi-density Scale-Independent Clustering Algorithm Based on DBSCAN Gholamreza Esfandani Hassan Abolhassani 202

An Efficient Algorithm for Mining Erasable Itemsets Zhihong Deng Xiaoran Xu 214

Discord Region Based Analysis to Improve Data Utility of Privately Published Time Series Shuai Jin Yubao Liu Zhijie Li 226

Deep Web Sources Classifier Based on DSOM-EACO Clustering Model Yong Feng Xianyong Chen Zhen Chen 238

Kernel Based K-Medoids for Clustering Data with Uncertainty Baoguo Yang Yang Zhang 246

Frequent Pattern Mining Using Modified CP-Tree for Knowledge Discovery R. Vishnu Priya A. Vadivel R.S. Thakur 254

Spatial Neighborhood Clustering Based on Data Field Meng Fang Shuliang Wang Hong Jin 262

Surrounding Influenced K-Nearest Neighbors: A New Distance Based Classifier I. Mendialdua B. Sierra E. Lazkano I. Irigoien E. Jauregi 270

A Centroid K-Nearest Neighbor Method Qingjiu Zhang Shiliang Sun 278

Mining Spatial Association Rules with Multi-relational Approach Min Qian Li-Jie Pu Rong Fu Ming Zhu 286

An Unsupervised Classification Method of Remote Sensing Images Based on Ant Colony Optimization Algorithm Duo Wang Bo Cheng 294

A Novel Clustering Algorithm Based on Gravity and Cluster Merging Jiang Zhong Longhai Liu Zhiguo Li 302

II Data Mining in Specific Areas

Evolution Analysis of a Mobile Social Network Hao Wang Alvin Chin 310

Distance Distribution and Average Shortest Path Length Estimation in Real-World Networks Qi Ye Bin Wu Bai Wang 322

Self-adaptive Change Detection in Streaming Data with Non-stationary Distribution Xiangliang Zhang Wei Wang 334

Anchor Points Seeking of Large Urban Crowd Based on the Mobile Billing Data Wenhao Huang Zhengbin Dong Nan Zhao Hao Tian Guojie Song Guanhua Chen Yun Jiang Kunqing Xie 346

Frequent Pattern Trend Analysis in Social Networks Puteri N.E. Nohuddin Rob Christley Frans Coenen Yogesh Patel Christian Setzkorn Shane Williams 358

Efficient Privacy-Preserving Data Mining in Malicious Model Keita Emura Atsuko Miyaji Mohammad Shahriar Rahman 370

Analyze the Wild Birds' Migration Tracks by MPI-Based Parallel Clustering Algorithm HaiMing Zhang YuanChun Zhou JianHui Li XueZhi Wang BaoPing Yan 383

Formal Concept Analysis Based Clustering for Blog Network Visualization Jing Gao Wei Lai 394

Finding Frequent Subgraphs in Longitudinal Social Network Data Using a Weighted Graph Mining Approach Chuntao Jiang Frans Coenen Michele Zito 405

Weigted-FP-Tree Based XML Query Pattern Mining Mi Sug Gu Jeong Hee Hwang Keun Ho Ryu 417

Privacy-Preserving Data Mining in Presence of Covert Adversaries Atsuko Miyaji Mohammad Shahriar Rahman 429

Multiple Level Views on the Adherent Cohesive Subgraphs in Massive Temporal Call Graphs Qi Ye Bin Wu Bai Wang 441

Combating Link Spam by Noisy Link Analysis Yitong Wang Xiaofei Chen Xiaojun Feng 453

High Dimensional Image Categorization François Poulet Nguyen-Khang Pham 465

Efficiently Mining Co-Location Rules on Interval Data Lizhen Wang Hongmei Chen Lihong Zhao Lihua Zhou 477

Multiple Attribute Frequent Mining-Based for Dengue Outbreak Zalizah Awang Long Azuraliza Abu Bakar Abdul Razak Hamdan Mazrura Sahani 489

A Top-Down Approach for Hierarchical Cluster Exploration by Visualization Ke-Bing Zhang Mehmet A. Orgun Peter A. Busch Abhaya C. Nayak 497

Distributed Frequent Items Detection on Uncertain Data Shuang Wang Guoren Wang Jitong Chen 509

Mining Uncertain Sentences with Multiple Instance Learning Feng Ji Xipeng Qiu Xuanjing Huang 521

WeightLOFCC: A Heuristic Weight-Setting Strategy of LOF Applied to Outlier Detection in Time Series Data Hongrui Xie Yujiu Yang Wenhuang Liu 529

TGP: Mining Top-K Frequent Closed Graph Pattern without Minimum Support Yuhua Li Quan Lin Ruixuan Li Dongsheng Duan 537

Research on Similarity Matching for Multiple Granularities Time-Series Data Wenning Hao Enlai Zhao Hongjun Zhang Gang Chen Dawei Jin 549

A Novel Algorithm for Hierarchical Community Structure Detection in Complex Networks Chuan Shi Jian Zhang Liangliang Shi Yanan Cai Bin Wu 557

Investigating Sequential Patterns of DNS Usage and Its Applications Jun Wu Xin Wang Xiaodong Lee Baoping Yan 565

Key Issues and Theoretical Framework on Moving Objects Data Mining Rong Xie Xin Luo 577

An Improved KNN Based Outlier Detection Algorithm for Large Datasets Qian Wang Min Zheng 585

Some Developments of Determinacy Analysis Rein Kuusik Grete Lind 593

A New Computational Framework for Gene Expression Clustering Shahreen Kasim Safaai Deris Razib M. Othman 603

Forecasting Short-Term Trends of Stock Markets Based on Fuzzy Frequent Pattern Tree Defu Zhang Bo Wu Xian Hua Yangbin Yang 611

Inspired Rule-Based User Identification Peng Yang Yan Zheng 618

Author Index 625

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