Classification by clustering decision tree-like classifier based on adjusted clusters

Authors:
Barak Aviad;Gelbard Roy
Affiliations:
Information System Program, Graduate School of Business Administration, Bar-Ilan University, Ramat-Gan 52900, Israel;Information System Program, Graduate School of Business Administration, Bar-Ilan University, Ramat-Gan 52900, Israel
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 11
Cited 4

A statistical perspective on knowledge discovery in databases

Advances in knowledge discovery and data mining
Data mining (Invited talk. Abstract only): crossing the Chasm

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data clustering: a review

ACM Computing Surveys (CSUR)
Data Mining by Means of Binary Representation: A Model for Similarity and Clustering

Information Systems Frontiers
Fast and Robust General Purpose Clustering Algorithms

Data Mining and Knowledge Discovery
A database clustering methodology and tool

Information Sciences—Informatics and Computer Science: An International Journal
Traffic classification using clustering algorithms

Proceedings of the 2006 SIGCOMM workshop on Mining network data
A co-evolving decision tree classification method

Expert Systems with Applications: An International Journal
Investigating diversity of clustering methods: An empirical comparison

Data & Knowledge Engineering
Mining Supervised Classification Performance Studies: A Meta-Analytic Investigation

Journal of Classification
Review: Application of data mining techniques in customer relationship management: A literature review and classification

Expert Systems with Applications: An International Journal

An improved CART decision tree for datasets with irrelevant feature

SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
A decision support method, based on bounded rationality concepts, to reveal feature saliency in clustering problems

Decision Support Systems
Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks

Expert Systems with Applications: An International Journal
Integrating global and local application of random subspace ensemble

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	12.05

Visualization

Abstract

Currently cluster analysis techniques are used mainly to aggregate objects into groups according to similarity measures. Whether the number of groups is pre-defined (supervised clustering) or not (unsupervised clustering), clustering techniques do not provide decision rules or a decision tree for the associations that are implemented. The current study proposes and evaluates a new technique to define decision tree based on cluster analysis. The proposed model was applied and tested on two large datasets of real life HR classification problems. The results of the model were compared to results obtained by conventional decision trees. It was found that the decision rules obtained by the model are at least as good as those obtained by conventional decision trees. In some cases the model yields better results than decision trees. In addition, a new measure is developed to help fine-tune the clustering model to achieve better and more accurate results.