Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings (Lecture Notes in Computer Science (3056))

Thepaci?c-asiaconferenceonknowledgediscoveryanddatamining(pakdd) Has Been Held Every Year Since 1997. This Year, The Eighth In The Series (pakdd 2004) Was Held At Carlton Crest Hotel, Sydney, Australia, 26–28 May 2004. Pakdd Is A Leading International Conference In The Area Of Data Mining. It P- Vides An International Forum For Researchers And Industry Practitioners To Share Their New Ideas, Original Research Results And Practical Development Experiences From All Kdd-related Areas Including Data Mining, Data Warehousing, Machine Learning, Databases, Statistics, Knowledge Acquisition And Automatic Scienti?c Discovery, Data Visualization, Causal Induction, And Knowledge-based Systems. The Selection Process This Year Was Extremely Competitive. We Received 238 Researchpapersfrom23countries,whichisthehighestinthehistoryofpakdd, And Re?ects The Recognition Of And Interest In This Conference. Each Submitted Research Paper Was Reviewed By Three Members Of The Program Committee. F- Lowing This Independent Review, There Were Discussions Among The Reviewers, And When Necessary, Additional Reviews From Other Experts Were Requested. A Total Of 50 Papers Were Selected As Full Papers (21%), And Another 31 Were Selected As Short Papers (13%), Yielding A Combined Acceptance Rate Of Approximately 34%. The Conference Accommodated Both Research Papers Presenting Original - Vestigation Results And Industrial Papers Reporting Real Data Mining Applications Andsystemdevelopmentexperience.theconferencealsoincludedthreetutorials On Key Technologies Of Knowledge Discovery And Data Mining, And One Workshop Focusing On Speci?c New Challenges And Emerging Issues Of Knowledge Discovery Anddatamining.thepakdd2004programwasfurtherenhancedwithkeynote Speeches By Two Outstanding Researchers In The Area Of Knowledge Discovery And Data Mining: Philip Yu, Manager Of Software Tools And Techniques, Ibm T.j. Invited Speeches -- Session 1a: Classification (i) -- Session 1b: Clustering (i) -- Session 1c: Association Rules (i) -- Session 2a: Novel Algorithms (i) -- Session 2b: Association (ii) -- Session 2c: Classification (ii) -- Session 3a: Event Mining, Anomaly Detection, And Intrusion Detection -- Session 3b: Ensemble Learning -- Session 3c: Bayesian Network And Graph Mining -- Session 3d: Text Mining (i) -- Session 4a: Clustering (ii) -- Session 4b: Association (iii) -- Session 4c: Novel Algorithms (ii) -- Session 4d: Multimedia Mining -- Session 5a: Text Mining And Web Mining (ii) -- Session 5b: Statistical Methods, Sequential Data Mining, And Time Series Mining -- Session 5c: Novel Algorithms (iii) -- Session 5d: Biomedical Mining. Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang (eds.). Includes Bibliographical References And Index. Also Issued Online. This book constitutes the refereed proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data mining, PAKDD 2004, beld in Sydney, Australia in May 2004. The 50 revised full papers and 31 revised short papers presented were carefully reviewed and selected from a total of 238 submissions. The papers are organized in topical sections on classification; clustering; association rules; novel algorithms; event mining, anomaly detection, and intrusion detection; ensemble learning; Bayesian network and graph mining; text mining; multimedia mining; text mining and Web mining; statistical methods, sequential data mining, and time series mining; and biomedical data mining. This book constitutes the refereed proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data mining, PAKDD 2004, held in Sydney, Australia in May 2004. The 50 revised full papers and 31 revised short papers presented were carefully reviewed and selected from a total of 238 submissions. The papers are organized in topical sections on classification; clustering; association rules; novel algorithms; event mining, anomaly detection, and intrusion detection; ensemble learning; Bayesian network and graph mining; text mining; multimedia mining; text mining and Web mining; statistical methods, sequential data mining, and time series mining; and biomedical data mining While some interesting progress has been achieved over the past few years, especially when it comes to techniques and scalable algorithms, very few organizations have managed to benefit from the technology.