A Survival Study on Data Structure Based Clustering Techniques for Multidimensional Data Stream Analysis
DOI:
https://doi.org/10.26438/ijcse/v5i12.101108Keywords:
Data stream, Multidimensional data, Data mining, Data structure, Subspace clusteringAbstract
Data mining plays an effective role in the field of computer science to analysis the data objects. The data mining process is used to mine the knowledge from huge database. Then, the extracted information is modified into an understandable data structure for the future analysis. The data structure in a computer is an essential approach to categorize and manage the data which is utilized for efficient usage. The data stream is referred as a structured sequence of instances; the data stream mining discovers the knowledge structures from continuous and fast data records. The clustering is the process of creating the group by collecting the data of similar patterns and also describes the meaningful structure of data. The additional process of traditional clustering termed as Subspace Clustering which is utilized for detecting the clusters in various subspaces within dataset. Then, the subspace clustering algorithms are introduced to discover the cluster in multiple overlapping subspaces by searching the relevant dimensions. Many research works are developed for managing the high dimensional data with the objective of providing better improvement on minimizing the performance of dimensionality and enhancing the clustering accuracy. However, the existing works failed to reduce the space complexity. Therefore, the research work focuses on reducing the dimensionality with improved clustering accuracy by executing the clustering and subspace clustering for data stream with data structure techniques.
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