JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Clustering method for similar user with Miexed Data in SNS
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Clustering method for similar user with Miexed Data in SNS
Song, Hyoung-Min; Lee, Sang-Joon; Kwak, Ho-Young;
  PDF(new window)
 Abstract
The enormous increase of data with the development of the information technology make internet users to be hard to find suitable information tailored to their needs. In the face of changing environment, the information filtering method, which provide sorted-out information to users, is becoming important. The data on the internet exists as various type. However, similarity calculation algorithm frequently used in existing collaborative filtering method is tend to be suitable to the numeric data. In addition, in the case of the categorical data, it shows the extreme similarity like Boolean Algebra. In this paper, We get the similarity in SNS user's information which consist of the mixed data using the Gower's similarity coefficient. And we suggest a method that is softer than radical expression such as 0 or 1 in categorical data. The clustering method using this algorithm can be utilized in SNS or various recommendation system.
 Keywords
Similarity;Clustering;Mixed data;Gower's similarity coefficient;
 Language
Korean
 Cited by
 References
1.
"The Digital Universe of Opportunities", EMC & IDC, 2014

2.
Soojin Lee, Taeryong Jeon, Gyeongdong Baek, Sungshin Kim, "A Movie Rating Prediction System of User Propensity Analysis based on Collaborative Filtering and Fussy System", Journal of Intelligence and Information System, Vol. 19, No. 2, pp. 242-247, 2009.

3.
Yeo-Kwang Yoon, "A Study on Contents Curation of Portal Sites", Journal of the Korea Entertainment Industry Association(JKEIA), Vol. 8, No. 4, pp. 31-43, Dec. 2014.

4.
Ahn Hyung Jun, "A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem", Information Sciences, Vol. 178, No. 1, pp.37-51, 2007.

5.
Hyeongdo Kim, "Collaborative Tag-Based Recommendation Methods Using the Principle of Latent Factor Models", Journal of Society for e-Business Studies, Vol. 14, No. 4, pp.47-57, Nov. 2009.

6.
Hyeong-Joon Kwon, Kwang-Seok Hong, "Personalization of LBS using Recommender Systems Based on Collaborative Filtering", Journal of Korean Society for Internet Information Vol. 11 No. 6, pp.1-11, Dec. 2010.

7.
Galit Shmueli, Nitin R. Patel, Peter C. Bruce, "Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Exel with XLMiner", Wiley, p.299, 2006.

8.
Dongjin Park, Ingeuk Hwang, Teahun Ann, "A Clustering Algorithm for Categorical Data Oriented Database", Korea Institute of Industrial Engineers Autumn Conference, pp.355-362, Oct. 1998.

9.
Shinwon Lee, "Comparison of Initial Seeds Methods for K-Means Clustering", Journal of Korean Society for Internet Information, Vol.13, No.6, pp.1-8, Dec. 2012.

10.
https://en.wikipedia.org/wiki/K-means_clustering