Multi-Resolution Hierarchical Structure for Efficient Data Aggregation and Mining of Big Data

Alwajidi, Safaa; Li, Yang

doi:10.1109/icactm.2019.8776717

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…In our previous work [8], we have developed an effective technique to create, organize, access, and maintain a tree summarization structure for efficient processing of large data sets. The developed tree structure is incrementally updated and provides the necessary information for many data mining and learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation

Alwajidi¹,

Yang²

2020

Adv. sci. technol. eng. syst. j.

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Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such as decision tree and nearest neighbor search. The proposed method can handle streaming data efficiently and, for entropy discretization, provide sufficient information to find the optimal split variable and the optimal split value.

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Section: Introductionmentioning

confidence: 99%