The Inmates Are Running the Asylum
The Inmates Are Running the Asylum
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Cluster Validation with Generalized Dunn's Indices
ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
CLIHC '05 Proceedings of the 2005 Latin American conference on Human-computer interaction
QROCK: A quick version of the ROCK algorithm for clustering of categorical data
Pattern Recognition Letters
Cluster validity measurement techniques
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
A expressão da diversidade de usuários no projeto de interação com padrões e personas
Proceedings of the VIII Brazilian Symposium on Human Factors in Computing Systems
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Who uses web search for what: and how
Proceedings of the fourth ACM international conference on Web search and data mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidirectional knowledge extraction process for creating behavioral personas
Proceedings of the 10th Brazilian Symposium on on Human Factors in Computing Systems and the 5th Latin American Conference on Human-Computer Interaction
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There is much information of users to be analyzed to develop a personalized project. To perform an analysis, it is necessary to create clusters in order to identify features to be explored by the project designer. In general, a classical clustering algorithm called K-Means is used to group users features. However, K-Means reveals some problems during the cluster process. In fact, K-Means does not guarantee to find Quality-Preserved Sets (QPS) and its randomness let the entire process unpredictable and unstable. In order to avoid these problems, a novel algorithm called Q-SIM (Quality Similarity Clustering) is presented in this paper. The Q-SIM algorithm has the objective to keep a similarity degree among all elements inside the cluster and guarantee QPS for all sets. During the tests, Q-SIM demonstrates that it is better than k-means and it is more appropriate to solve the problem for user modeling presented in this paper.