Sebanyak 3 item atau buku ditemukan

2001 IEEE International Conference on Data Mining

Proceedings : 29 November-2 December, 2001, San Jose, California

This proceedings of the November 2001 conference explores the design, analysis and implementation of data mining theory and systems. The 72 regular papers and 37 posters discuss data mining algorithms, data and knowledge representation, modeling of data to support data mining, scalability issues, st

That means mining with low support threshold may lead to more pattems frequent
in some partitions. On the other hand, less memory (small partition) leads to more
partitions and also increase the ratio. As shown in the figure, only a limited ...

Research and Development in Knowledge Discovery and Data Mining

Second Pacific-Asia Conference, PAKDD'98, Melbourne, Australia, April 15-17, 1998, Proceedings

This book constitutes the refereed proceedings of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD-98, held in Melbourne, Australia, in April 1998. The book presents 30 revised full papers selected from a total of 110 submissions; also included are 20 poster presentations. The papers contribute new results to all current aspects in knowledge discovery and data mining on the research level as well as on the level of systems development. Among the areas covered are machine learning, information systems, the Internet, statistics, knowledge acquisition, data visualization, software reengineering, and knowledge based systems.

This book constitutes the refereed proceedings of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD-98, held in Melbourne, Australia, in April 1998.

The Top Ten Algorithms in Data Mining

Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm. The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics—including classification, clustering, statistical learning, association analysis, and link mining—in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses. By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.

By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications.