JobMiner: a real-time system for mining job-related patterns from social media
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback-driven multiclass active learning for data streams
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Bootstrapping active name disambiguation with crowdsourcing
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
Clustering similar items for web text has become increasingly important in many Web and Information Retrieval applications. For several kinds of web text data, it is much easier to obtain some external information other than textual features which can be utilized to improve the performance of clustering analysis. This external information, called prior information, indicates label sign and pair wise constraints on sample points. We propose a unifying framework that can incorporate prior information of cluster membership for web text cluster analysis and develop a novel semi-supervised clustering model. The proposed framework offers several advantages over existing semi-supervised approaches. First, most previous work handles labeled data by converting it to pair wise constraints and thus leads to much more computation. The proposed approach can handle pair wise constraints together with labeled data simultaneously so that the computation is greatly reduced. Second, the framework allows us to obtain these prior information automatically or only with little human effort, thus, making it possible to boost the clustering learning performance relatively easily. We evaluated the proposed method on the real-world problems of automatically grouping online news feeds and web blog messages. Experimental results indicate the proposed framework incorporating prior information can indeed lead to statistically significant clustering improvements over the performance of approaches access only to textual features.