Fine‐Grained Mobile Application Clustering Model Using Retrofitted Document Embedding
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 clus...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Electronics and Telecommunications Research Institute (ETRI)
2017-08-01
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| Series: | ETRI Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.4218/etrij.17.0116.0936 |
| _version_ | 1811293212944367616 |
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| author | Yeo‐Chan Yoon Junwoo Lee So‐Young Park Changki Lee |
| author_facet | Yeo‐Chan Yoon Junwoo Lee So‐Young Park Changki Lee |
| author_sort | Yeo‐Chan Yoon |
| collection | DOAJ |
| description | 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 K‐means algorithm. In addition, the mean average precision improved by more than 0.09 in a comparison with a support vector machine classifier. |
| first_indexed | 2024-04-13T04:58:11Z |
| format | Article |
| id | doaj.art-88d29d1ce82046a0b31accd77451ebfe |
| institution | Directory Open Access Journal |
| issn | 1225-6463 2233-7326 |
| language | English |
| last_indexed | 2024-04-13T04:58:11Z |
| publishDate | 2017-08-01 |
| publisher | Electronics and Telecommunications Research Institute (ETRI) |
| record_format | Article |
| series | ETRI Journal |
| spelling | doaj.art-88d29d1ce82046a0b31accd77451ebfe2022-12-22T03:01:25ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632233-73262017-08-0139444345410.4218/etrij.17.0116.093610.4218/etrij.17.0116.0936Fine‐Grained Mobile Application Clustering Model Using Retrofitted Document EmbeddingYeo‐Chan YoonJunwoo LeeSo‐Young ParkChangki LeeIn 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 K‐means algorithm. In addition, the mean average precision improved by more than 0.09 in a comparison with a support vector machine classifier.https://doi.org/10.4218/etrij.17.0116.0936Document embeddingText clusteringDeep learning |
| spellingShingle | Yeo‐Chan Yoon Junwoo Lee So‐Young Park Changki Lee Fine‐Grained Mobile Application Clustering Model Using Retrofitted Document Embedding ETRI Journal Document embedding Text clustering Deep learning |
| title | Fine‐Grained Mobile Application Clustering Model Using Retrofitted Document Embedding |
| title_full | Fine‐Grained Mobile Application Clustering Model Using Retrofitted Document Embedding |
| title_fullStr | Fine‐Grained Mobile Application Clustering Model Using Retrofitted Document Embedding |
| title_full_unstemmed | Fine‐Grained Mobile Application Clustering Model Using Retrofitted Document Embedding |
| title_short | Fine‐Grained Mobile Application Clustering Model Using Retrofitted Document Embedding |
| title_sort | fine grained mobile application clustering model using retrofitted document embedding |
| topic | Document embedding Text clustering Deep learning |
| url | https://doi.org/10.4218/etrij.17.0116.0936 |
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