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On exploiting the power of time in data mining
We introduce the new paradigm of Change Mining as data mining over a volatile, evolving world with the objective of understanding change. While there is much work on incremental mining and stream mining, both focussing on the adaptation of patterns to a ...
A brief survey on anonymization techniques for privacy preserving publishing of social network data
Nowadays, partly driven by many Web 2.0 applications, more and more social network data has been made publicly available and analyzed in one way or another. Privacy preserving publishing of social network data becomes a more and more important concern. ...
Web data mining: exploring hyperlinks, contents, and usage data
This paper presents a review of the book "Web Data Mining - Exploring Hyperlinks, Contents, and Usage Data" by Bing Liu. The review concludes that the breadth and depth of this book makes it a required staple for every Web mining researcher, student, or ...
Incremental pattern discovery on streams, graphs and tensors
Incremental pattern discovery targets streaming applications where the data continuously arrive incrementally. The questions are how to find patterns (main trends) incrementally; or how to efficiently update the old patterns when new data arrive; or how ...
KDD cup 2008 and the workshop on mining medical data
In this report we summarize the KDD Cup 2008 task, which addressed a problem of early breast cancer detection. We describe the data and the challenges, the results and summarize the algorithms used by the winning teams. We also summarize the workshop on ...
Breast cancer identification: KDD CUP winner's report
We describe the ideas and methodologies that we developed in addressing the KDD Cup 2008 on early breast cancer detection, and discuss how they contributed to our success. The most important components of our solution were 1) the identification of ...
Learning to improve area-under-FROC for imbalanced medical data classification using an ensemble method
- Hung-Yi Lo,
- Chun-Min Chang,
- Tsung-Hsien Chiang,
- Cho-Yi Hsiao,
- Anta Huang,
- Tsung-Ting Kuo,
- Wei-Chi Lai,
- Ming-Han Yang,
- Jung-Jung Yeh,
- Chun-Chao Yen,
- Shou-De Lin
This paper presents our solution for KDD Cup 2008 competition that aims at optimizing the area under ROC for breast cancer detection. We exploited weighted-based classification mechanism to improve the accuracy of patient classification (each patient is ...
Report on the second KDD workshop on data mining for advertising
Following the success of our first workshop, we organized ADKDD 2008 1 - the second International Workshop on Data Mining and Audience Intelligence for Advertising, in conjunction with KDD 2008 at Las Vegas, Nevada, USA. This report is a summary of the ...
KDD2008 workshop report DMMT'08: data mining using matrices and tensors
We provide a summary of theWorkshop on Data Mining Using Matrices and Tensors (DMMT'08) held in conjunction with ACM SIGKDD 2008, on August 24th in Las Vegas, USA. About 100 people attended the workshop. We report in detail about the research issues ...
Algorithmic and statistical challenges in modern largescale data analysis are the focus of MMDS 2008
We provide a report for the ACM SIGKDD community about the 2008 Workshop on Algorithms for Modern Massive Data Sets (MMDS 2008), its origin in MMDS 2006, and future directions for this interdisciplinary research area.
MMIS07, 08: mining multiple information sources workshop report
In this report, we summarize the research issues, contents, and outcomes of the two recent workshops on Mining Multiple Information Sources (MMIS-07, 08) collocated with the 13th and the 14th ACM SIGKDD International Conference on Knowledge Discovery ...
PinKDD'08: privacy, security, and trust in KDD post workshop report
This report summarizes the events of the 2nd International Workshop on Privacy, Security, and Trust in KDD, at the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. The workshop was held on August 24, 2008 in Las Vegas, ...
Knowledge discovery from sensor data (SensorKDD)
- Ranga Raju Vatsavai,
- Olufemi A. Omitaomu,
- Joao Gama,
- Nitesh V. Chawla,
- Mohamed Medhat Gaber,
- Auroop R. Ganguly
Extracting knowledge and emerging patterns from sensor data is a nontrivial task. The challenges for the knowledge discovery community are expected to be immense. On one hand, dynamic data streams or events require real-time analysis methodologies and ...
SNAKDD 2008 social network mining and analysis postworkshop report
In this report, we summarize the contents and outcomes of the recent SNAKDD 2008 workshop on Social Network Mining and Analysis that was held in conjunction with the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ...
WebKDD 2008: 10 years of knowledge discovery on the web post-workshop report
WebKDD was held in conjunction with the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), August 24, 2008, in Henderson/Las Vegas, Nevada. In 2008, WebKDD was held for the tenth time. We report on the contents ...
Retail sales prediction and item recommendations using customer demographics at store level
This paper outlines a retail sales prediction and product recommendation system that was implemented for a chain of retail stores. The relative importance of consumer demographic characteristics for accurately modeling the sales of each customer type ...
Learning preferences of new users in recommender systems: an information theoretic approach
Recommender systems are an effective tool to help find items of interest from an overwhelming number of available items. Collaborative Filtering (CF), the best known technology for recommender systems, is based on the idea that a set of like-minded ...
