BROWSE
Detail
Journal Article
Fine-Grained Mobile Application Clustering Model Using Retrofitted Document Embedding
- Authors
- Yeo-Chan Yoon, Junwoo Lee, So-Young Park, Changki Lee
- Issue Date
- 2017-08
- Citation
- ETRI Journal, v.39, no.4, pp.443-454
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- 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 Keywords
- 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
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
