Concurrent goal-oriented co-clustering generation in social networks

Abstract

Recent years, social network has attracted many attentions from research communities in data mining, social science and mobile etc, since users can create different types of information due to different actions and the information gives us the opportunities to better understand the insights of people's social lives. Co-clustering is an important technique to detect patterns and phenomena of two types of closely related objects. For example, in a location based social network, places can be clustered with regards to location and category, respectively and users can be clustered w.r.t. their location and interests, respectively. Therefore, there are usually some latent goals behind a co-clustering application. However, traditionally, co-clustering methods are not specifically designed to handle multiple goals. That leaves certain drawbacks, i.e., it cannot guarantee that objects satisfying each individual goal would be clustered into the same cluster. However, in many cases, clusters of objects meeting the same goal is required, e.g., a user may want to search places within one category but in different locations. In this paper, we propose a goal-oriented co-clustering model, which could generate co-clusterings with regards to different goals simultaneously. By this method, we could get co-clusterings containing objects with desired aspects of information from the original data source. Seed features sets are pre-selected to represent goals of co-clusterings. By generating expanded feature sets from seed feature sets, the proposed model concurrently co-clustering objects and assigning other features to different feature clusters.