Hard and Soft (Fuzzy) clustering analysis of Claustrophobia questionnaire data
Here, I tried to implement all methods of clustering, which is used in one of my papers published in Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation.
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A type of anxiety which exhibits when the person is placed in an enclosed area is called Claustrophobia. To assess its severity a questionnaire called Claustrophobia Questionnaire (CLQ) was proposed. The Persian translation of this questionnaire was filled out with 355 people (139 females, 216 males) which were mostly university students. The purpose of this study was to distinguish between claustrophobic and non-claustrophobic participants using machine learning algorithms. To separate these two subtypes, a naturally fit algorithm seems to be fuzzy clustering, which is considered as the main focus. The results were compared to a few classic hard clustering algorithms to assess the performance of this method. Finally, to validate the outcome in separation of participants to claustrophobics and non-claustrophobics, a small subgroup of data with self-report on classification were used. The results of this ongoing study show a clear difference between male and female participants in terms of cluster patterns. The outcome seems to be promising in terms of successful separation of the two clusters, and hence can be used as a screening tool in Persian speaking anxiety treatment centers.
