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(searched for: doi:10.13176/11.459)
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, , Asit K. Das, Kuntal Ghosh, Late C. A. Murthy
Published: 6 January 2020
Electronic Commerce Research, Volume 21, pp 917-954; https://doi.org/10.1007/s10660-019-09395-y

Abstract:
Automated community detection is an important problem in the study of complex networks. The idea of community detection is closely related to the concept of data clustering in pattern recognition. Data clustering refers to the task of grouping similar objects and segregating dissimilar objects. The community detection problem can be thought of as finding groups of densely interconnected nodes with few connections to nodes outside the group. A node similarity measure is proposed here that finds the similarity between two nodes by considering both neighbors and non-neighbors of these two nodes. Subsequently, a method is introduced for identifying communities in complex networks using this node similarity measure and the notion of data clustering. The significant characteristic of the proposed method is that it does not need any prior knowledge about the actual communities of a network. Extensive experiments on several real world and artificial networks with known ground-truth communities are reported. The proposed method is compared with various state of the art community detection algorithms by using several criteria, viz. normalized mutual information, f-measure etc. Moreover, it has been successfully applied in improving the effectiveness of a recommender system which is rapidly becoming a crucial tool in e-commerce applications. The empirical results suggest that the proposed technique has the potential to improve the performance of a recommender system and hence it may be useful for other e-commerce applications.
, C. A. Murthy
International Journal of Machine Learning and Cybernetics, Volume 7, pp 877-892; https://doi.org/10.1007/s13042-015-0421-y

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Sosuke Amano, , Makoto Ogawa
2015 IEEE International Conference on Multimedia Big Data pp 48-55; https://doi.org/10.1109/bigmm.2015.54

Abstract:
Food Log is a multimedia recording tool for producing food records for many individuals. In one year of operation, Food Log has produced more than one million food records for meals eaten by users. We found nearly 70,000 unique food records among these data. In analyzing them, one of the challenges is to extract meal categories from such a large number of records. In this paper, we propose a method for compressing a meal name into a shorter representation. First, we collect similar meal names using a k-nearest neighbor search. Next, we construct a word graph to model the relationship between the meal names and items in the database. We select representative words by identifying minimal paths in the word graph. Finally, we obtain a few words that represent categorical information about the original meal name. We applied the method to data in food records for both Food Log and the Rakuten Recipe database. Our results show that the method worked effectively for both datasets.
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