Parmtrd: Parallel Association Rules Based Multiple-Topic Relationships Detection

Lots of events happened everyday make social big data have plenty of topics. A topic usually comprises a series of stories. Clues of associations among stories are usually clear, but hidden associations among topics are not always intuitive. It is challenging to find topic associations due to intrinsic complexities of social big data, while analyzing relationships among topics is valuable to explore and reach to origination sources of specific events. Existing research rarely pay attention to analyze multiple-topic relationships. This paper proposes a mining approach for topic relationships detection based on parallel association rules, namely PARMTRD (Parallel Association Rules based Multiple-Topic Relationships Detection). PARMTRD obtains association keyword sets for each topic using parallel association rules based on large-scale frequent keyword sets, which mines association rules for multiple topics in parallel. PARMTRD detects the relevance among multiple topics by selecting and assembling association keywords from association keyword sets, which help to find sources of events. Experiments show that PARMTRD can detect the hidden relationships among multiple topics accurately and efficiently.