ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A top-down approach for density-based clustering using multidimensional indexes
Journal of Systems and Software - Special issue: Performance modeling and analysis of computer systems and networks
Student social graphs: visualizing a student's online social network
CSCW '04 Proceedings of the 2004 ACM conference on Computer supported cooperative work
QROCK: A quick version of the ROCK algorithm for clustering of categorical data
Pattern Recognition Letters
| Hi-index | 0.00 |
In this paper we propose a novel approach that identifies meaningful student groups more objectively. As the data for objective analysis, we use communication history recordsthat are collected from various communication tools such as telephones, e-mails, and messengers. We use the simple intuition that communication history records implicitly contain peer relationship information. We first formally define the notion of degree of familiaritybetween students and present mathematical formulas that compute the degree based on the history records. We then adopt a clustering technique to mine meaningful groups. To use the clustering technique, we define the measure of similaritybetween friends based on the degree of familiarity, and perform clustering using the measure. To show the practicality of the proposed method, we have implemented it and interpreted the meaning of experimental results.