Recent Advances in Optimization Models for Data Mining: Clustering, Feature Selection and Classification

Fan, Y-j
İyigün, Cem
Chaovalitwongse , W. A.
Data mining aims at finding interesting, useful or profitable information in very large databases. The enormous increase in the size of available scientific and commercial databases (data avalanche) as well as the continuing and exponential growth in performance of present day computers make data mining a very active field. In many cases, the burgeoning volume of data sets has grown so large that it threatens to overwhelm rather than enlighten scientists. Therefore, traditional methods are revised and streamlined, complemented by many new methods to address challenging new problems. Mathematical Programming plays a key role in this endeavor. It helps us to formulate precise objectives (e.g., a clustering criterion or a measure of discrimination) as well as the constraints imposed on the solution (e.g., find a partition, a covering or a hierarchy in clustering). It also provides powerful mathematical tools to build highly performing exact or approximate algorithms. This book is based on lectures presented at the workshop on ""Data Mining and Mathematical Programming"" (October 10-13, 2006, Montreal) and will be a valuable scientific source of information to faculty, students, and researchers in optimization, data analysis and data mining, as well as people working in computer science, engineering and applied mathematics.


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Association rules are an important set of data mining results, which are helpful in handling large amount of data and extracting useful association information from them. There are many algorithms developed for finding interesting association rules and also some other algorithms for rule reduction purposes. All of the proposed methods have some strong and weak points, which can be useful according to their application areas. In the literature, there exist several comparison studies trying to find the best a...
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Marsh, William; Nur, Khalid; Yet, Barbaros; Majumdar, Arnab (2016-05-01)
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Among the areas of data and text mining which are employed today in OR, science, economy and technology, clustering theory serves as a preprocessing step in the data analyzing. An important component of clustering theory is determination of the true number of clusters. This problem has not been satisfactorily solved. In our paper, this problem is addressed by the cluster stability approach. For several possible numbers of clusters, we estimate the stability of the partitions obtained from clustering of samp...
Citation Formats
Y.-j. Fan, C. İyigün, and W. A. Chaovalitwongse, Recent Advances in Optimization Models for Data Mining: Clustering, Feature Selection and Classification. 2008, p. 95.