Data mining
A statistical perspective on knowledge discovery in databases
Advances in knowledge discovery and data mining
ACM Computing Surveys (CSUR)
A projection pursuit approach to variable selection
Computational Statistics & Data Analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 hybrid sales forecasting system based on clustering and decision trees
Decision Support Systems
A multicriteria decision support system for housing evaluation
Decision Support Systems
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
Weighted order-dependent clustering and visualization of web navigation patterns
Decision Support Systems
Mining Supervised Classification Performance Studies: A Meta-Analytic Investigation
Journal of Classification
Expert Systems with Applications: An International Journal
Visualization of multi-algorithm clustering for better economic decisions - The case of car pricing
Decision Support Systems
Classification by clustering decision tree-like classifier based on adjusted clusters
Expert Systems with Applications: An International Journal
Classification by clustering decision tree-like classifier based on adjusted clusters
Expert Systems with Applications: An International Journal
Adjusting Fuzzy Similarity Functions for use with standard data mining tools
Journal of Systems and Software
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In many real-life data mining problems, there is no a-priori classification (no target attribute that is known in advance). The lack of a target attribute (target column/class label) makes the division process into a set of groups very difficult to define and construct. The end user needs to exert considerable effort to interpret the results of diverse algorithms because there is no pre-defined reliable ''benchmark''. To overcome this drawback the current paper proposes a methodology based on bounded-rationality theory. It implements an S-shaped function as a saliency measure to represent the end user's logic to determine the features that characterize each potential group. The methodology is demonstrated on three well-known datasets from the UCI machine-learning repository. The grouping uses cluster analysis algorithms, since clustering techniques do not need a target attribute.