C4.5: programs for machine learning
C4.5: programs for machine learning
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A note on "beyond market baskets: generalizing association rules to correlations"
ACM SIGKDD Explorations Newsletter
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This paper proposes a mining novel approach which consists of two new data mining algorithms for the classification over quantitative data, based on two new pattern called MOUCLAS (MOUntain function based CLASsification) Patterns and JumpingMOUCLAS Patterns. The motivation of the study is to develop two classifiers for quantitative attributes by the concepts of the association rule and the clustering. An illustration of using petroleum well logging data for oil/gas formation identification is presented in the paper. MPsandJMPs are ideally suitable to derive the implicit relationship between measured values (well logging data) and properties to be predicted (oil/gas formation or not). As a hybrid of classification and clustering and association rules mining, our approach have several advantages which are (1) it has a solid mathematical foundation and compact mathematical description of classifiers, (2) it does not require discretization, (3) it is robust when handling noisy or incomplete data in high dimensional data space.