Fast Mining of Temporal Data Clustering

Temporal data clustering provides underpinning techniques for discovering the intrinsic structure and condensing information over temporal data. In this paper, we present a temporal data clustering framework via a weighted clustering produced by initial clustering analysis on different temporal data representations. In the existing system a novel weighted function guided by clustering validation criteria to reconcile initial partitions to candidate consensus partitions from different perspectives, and then, introduce an agreement function to further reconcile those candidate consensus partitions to a final partition.with the rapid growth of text documents, document clustering has become one of the main techniques for organizing large amount of documents into a small number of meaningful clusters. However, there still exist several challenges for document clustering, such as high dimensionality, scalability, accuracy, meaningful cluster labels, overlapping clusters, and extracting semantics from texts. In order to improve the quality of document clustering results, we propose an effective fast mining of temporal data clustering (fmtdc) approach that integrates association mining with an existing wordnet to alleviate these problems finally, each document is dispatched into more than one target cluster by referring to these candidate clusters, and then the highly similar target clusters are merged. The experimental results proved that our approach outperforms the influential document clustering methods with higher accuracy.