From Machine Learning to Data Mining

TL;DR: A systematic preprocessing procedure is applied to a known septic shock patient database and classification results are compared with previous studies and show that the preprocessing is crucial to improve classifiers accuracy.

TL;DR: In this paper, a web sitelerinin internet ziyaretcileri tarafindaki yansimasi ile olusan web sitesi istatistiklerinin bu sektordeki onemi artmaktadir, bu degerler butununun ag trafiginde anlamlandirilabilabilabilmesi icin siniflandirma veya kumeleme islemlerine tabi tutulmasi gerekmektedir.

TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.

TL;DR: This work deals with quantitative attributes by fine-partitioning the values of the attribute and then combining adjacent partitions as necessary and introduces measures of partial completeness which quantify the information lost due to partitioning.

TL;DR: In this paper, the problem of mining generalized association rules was introduced, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, finding associations between items at any level of the taxonomy.

TL;DR: New algorithms that reduce the database activity considerably by picking a Random sample, to find using this sample all association rules that probably hold in the whole database, and then to verify the results with the rest of the database.