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학술지 Fine-Grained Mobile Application Clustering Model Using Retrofitted Document Embedding
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저자
윤여찬, 이준우, 박소영, 이창기
발행일
201708
출처
ETRI Journal, v.39 no.4, pp.443-454
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.17.0116.0936
협약과제
16MS5600, 디지털콘텐츠 In-House R&D, 이준우
초록
In this paper, we propose a fine-grained mobile application clustering model using retrofitted document embedding. To automatically determine the clusters and their numbers with no predefined categories, the proposed model initializes the clusters based on title keywords and then merges similar clusters. For improved clustering performance, the proposed model distinguishes between an accurate clustering step with titles and an expansive clustering step with descriptions. During the accurate clustering step, an automatically tagged set is constructed as a result. This set is utilized to learn a high-performance document vector. During the expansive clustering step, more applications are then classified using this document vector. Experimental results showed that the purity of the proposed model increased by 0.19, and the entropy decreased by 1.18, compared with the Kmeans algorithm. In addition, the mean average precision improved by more than 0.09 in a comparison with a support vector machine classifier.
키워드
Deep learning, Document embedding, Text clustering
KSP 제안 키워드
Clustering model, Clustering performance, Document embedding, High performance, Mobile Application(APP), Proposed model, Support VectorMachine(SVM), Support vector Machine Classifier, Text Clustering, deep learning(DL), fine-grained
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