The Effect of “Directness” of the Distance Metric to Produce Compact and Accurate Associative Classification Models
Source abstract
Abstract
Machine learning techniques are ever prevalent as datasets continue to grow daily. Associative classification (AC), which combines classification and association rule mining algorithms, plays an important role in understanding big datasets that generate a large number of rules. Clustering, on the other hand, can contribute by reducing the rule space to produce compact models. The above-mentioned facts were the main motivation for this research work. We propose a new distance (similarity) metric based on “direct” and “indirect” measures and explain the overall importance of this method, which can produce compact and accurate models. Specifically, we aim to employ agglomerative hierarchical clustering to develop new associative classification models that contain a lower number of rules. Furthermore, a new strategy (based on the cluster center) is presented to extract the representative rule for each cluster. Twelve real-world datasets were evaluated experimentally for accuracy and compactness, and the results were compared to those of previously established associative classifiers. The results show that our method outperformed the other algorithms in terms of classifier size on most of the datasets, while still being as accurate in classification.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Data Mining Algorithms and Applications · Rough Sets and Fuzzy Logic · Advanced Clustering Algorithms Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Information Technologies Department, Urgench State University, Khamid Alimdjan 14, Urgench 220100, Uzbekistan
Engineering and Computer Science Department, California State University, Fullerton, 800 N State College Blvd, Fullerton, CA 92831, USA
Department of Information Sciences and Technologies, University of Primorska, Glagoljaška 8, 6000 Koper, Slovenia
AI Laboratory, Jožef Stefan Institute, Jamova Cesta 39, 1000 Ljubljana, Slovenia
Funding
the Ministry of “Innovative Development” of the Republic of Uzbekistan · National Science Foundation (NSF)
References (42)
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