Similar or Not Similar: This Is a Parameter Question
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.
KeywordsQ-SIM Clustering User Modeling Personas
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