{'fraction_non_alphanumeric': 0.02278481012658228, 'fraction_numerical': 0.0, 'mean_word_length': 5.084615384615384, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Clustering for user modeling in recommender e-commerce application: A RUP-based intelligent software life-cycle

In this paper we will present an RUP based software life cycle on how to incorporate a clustering algorithm on a prototype system. The process has four major steps. Firstly, designing and building in an adaptive application. Secondly, evaluating the system and through this process obtaining data for the clustering algorithms. Thirdly, compare the clustering algorithms with the above data as input. Fourthly, incorporate the best clustering algorithm into the system and building stereotypes. In our case we used an adaptive e-shop application as a test bed in order to apply these methods. The adaptive e-shop application that provides personalised recommendations to users.