Central problems in the management of innovation
Management Science
Information technology and work groups: the case of new product teams
Intellectual teamwork
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
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
Coordinating Expertise in Software Development Teams
Management Science
Software development: processes and performance
IBM Systems Journal
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
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A method for member selection of R&D teams using the individual and collaborative information
Expert Systems with Applications: An International Journal
Project teaming: Knowledge-intensive design for composing team members
Expert Systems with Applications: An International Journal
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
Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.