Title :
Concept Classification Using a Hybrid Data Mining Model
Author :
Brown, Sarah ; Forouraghi, Babak
Author_Institution :
Comput. Sci. Dept., St. Joseph´´s Univ., Philadelphia, PA, USA
Abstract :
Apriori is a well-known algorithm which is used extensively in market-basket analysis and data mining. The algorithm is used for learning association rules from transactional databases and is based on simple counting procedures. In this paper we propose enhancements to Apriori which allow it to perform concept classification similar to the way decision tree algorithms learn. Specifically, training examples are modified and treated as transactional data and the results are verified and further improved by C4.5 decision tree and k-means clustering algorithms, respectively. To demonstrate the novelty of the enhanced Apriori algorithm, we present a hybrid data mining model (HDMM) which identifies at-risk students based on their academic performance and other pertinent data.
Keywords :
data analysis; data mining; decision trees; pattern classification; pattern clustering; C4.5 decision tree; concept data classification; decision tree algorithms; enhanced Apriori algorithm; hybrid data mining model; k-means clustering algorithms; learning association rules; market-basket analysis; transactional databases; Algorithm design and analysis; Association rules; Classification tree analysis; Clustering algorithms; Data mining; Decision trees; Genetic algorithms; Itemsets; Support vector machine classification; Support vector machines; Data mining; classification; decision trees; learning;
Conference_Titel :
Tools with Artificial Intelligence, 2009. ICTAI '09. 21st International Conference on
Conference_Location :
Newark, NJ
Print_ISBN :
978-1-4244-5619-2
Electronic_ISBN :
1082-3409
DOI :
10.1109/ICTAI.2009.41