상세정보
학술지
Fine-Grained Mobile Application Clustering Model Using Retrofitted Document Embedding
- 발행일
- 201708
- 출처
- ETRI Journal, v.39 no.4, pp.443-454
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 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.
- KSP 제안 키워드
- Clustering model, Clustering performance, Document embedding, High performance, Mobile Application(APP), Proposed model, Support VectorMachine(SVM), Support vector Machine Classifier, fine-grained, mean average precision
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