JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Visualized Preference Transition Network Based on Recency and Frequency
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Visualized Preference Transition Network Based on Recency and Frequency
Masruri, Farid; Tsuji, Hiroshi; Saga, Ryosuke;
  PDF(new window)
 Abstract
Given a directed graph, we can determine how the user's preference moves from one product item to another. In this graph called "preference transition network", each node represents the product item while its edge pointing to the other nodes represents the transition of user's preference. However, with the large number of items make the network become more complex, unclear and difficult to be interpreted. In order to address this problem, this paper proposes a visualization technique in preference transition analysis based on recency and frequency. By adapting these two elements, the semantic meaning of each item and its transition can be clearly identified by its different types of node size, color and edge style. The experiment in a sales data has shown the results of the proposed approach.
 Keywords
Data Mining;Information Visualization;Preference Analysis;Graph Theory;RFM Analysis;
 Language
English
 Cited by
 References
1.
Aczel, A. D. and Sounderpandian, J. (2005), Complete Business Statistics, McGraw-Hill.

2.
Aggelis, V. and Christodoulakis, D. (2005), Customer Clustering using RFM Analysis, 9th WSEAS International Conference on Computers

3.
D'Auria, T. (2009), The Fall of RFM Analysis, http:// www.imn-unlocked.com/bi-creativecomputing/e_a rticle001467796.cfm?x = b11, 0, w.

4.
Graphviz, http://www.graphviz.org/.

5.
Han, J. and Kamber M. (2006), Data Mining: Concepts and Techniques, Elsevier Inc.

6.
Hayashi, Y., Saga, R., and Tsuji, H. (2009), Competition State Visualization for Sales Record Mining, Industrial, Engineering and Other Applications of Applied Intelligent Systems, 335-340.

7.
Hayashi, Y., Masruri, F., Saga, R., and Tsuji, H. (2010), Enhanced Visualization on Preference Transition for Sales Records, 8th IEEE International Conference on Industrial Informatics (INDIN2010), 361- 366.

8.
Keim, D. A. (2002), Information Visualization and Visual Data Mining, IEEE Trans. on Visualization and Computer Graphics, 8, 1-8. crossref(new window)

9.
Miglautsch, J. R. (2002), Thoughts on RFM scoring, The Journal of Database Marketing, 67-72.

10.
Northwind dataset, http://www.microsoft.com/.

11.
Ohsawa, Y., Benson, N. E., and Yachida, M. (1998), Key Graph: Automatic Indexing by Cooccurrence Graph based on Building Construction Metaphor, Proceedings. Advanced Digital Library Conference, 12-18.

12.
Saga, R., Terachi, M., Sheng, Z., and Tsuji, H. (2008), FACT-Graph: Trend Visualization by Frequency and Co-occurrence, KI 2008. Lecture Notes on Artificial Intelligence, Springer-Verlag Berlin Heidelberg, 5243, 308-315.

13.
Saga, R., Hayashi, Y., and Tsuji, H. (2008), Hotel Recommender System based on User's Preference Transition, IEEE International Conference on Systems, Man and Cybernetics (IEEE/SMC 2008), 2437- 2442.

14.
Terachi, M., Saga, R., Sheng, Z., and Tsuji, H. (2008), Visualized Technique for Trend Analysis of News Articles, Lecture Notes on Artificial Intelligence, Springer-Verlag Berlin Heidelberg, 5027, 659-668