Outlier Detection from Massive Short Documents using Domain Ontology
With the rapid development of information technology, huge data is accumulated. A vast amount of such data appears as short documents such as paper summary or conversations in open chatting rooms. It is useful to detect outliers from those documents in intelligence analysis applications. However, traditional outlier detecting methods based on vector space model can not get acceptable accuracy because the key words appear at low frequency. On the other hand, traditional outlier detecting algorithms become very inefficient or even unavailable when processing massive data. In this paper a density-based outlier detecting method using domain ontology is presented. This algorithm uses domain ontology to calculate the semantic distance between short documents which improves the accuracy. Parallel method is also used to get better performance and scalability.
massive short document outlier detection density domain ontology
Yonghcng Wang Shenghong Yang
School of Computer and Communication Hunan University Changsha, China
国际会议
厦门
英文
558-562
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）