Tweet Cluster Analyzer: Partition and Join-based Micro-clustering for Twitter Data Stream

Data stream mining is the process of extracting knowledge from continuously generated data. Since data stream processing is not a trivial task, the streams have to be analyzed with proper stream mining techniques. In many large volume of data stream processing, stream clustering helps to find the valuable hidden information. Many works have concentrated on clustering the data streams using various methods, but mostly those approaches lack in some core tasks needed to improve the cluster accuracy and quick processing of data streams. To tackle the problem of improving cluster quality and reducing the time for data stream processing time in cluster generation, the partition-based DBStream clustering method is proposed. The result has been compared with various data stream clustering methods, and it is evident from the experiments that the purity of clusters improves 5% and the time taken is reduced by 10% than the average time taken by other methods for clustering the data streams.

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