Efficient Discovery of Weighted Frequent Neighborhood Itemsets in Very Large Spatiotemporal Databases

IEEE Access

R. Uday Kiran¹  Koji Zettsu¹  P. P. C. Reddy²  P. Krishna Reddy²  Masashi Toyoda³  Masaru Kitsuregawa³ 

[1] Big Data Analytics Laboratory, National Institute of Information and Communications Technology, Tokyo, Japan;Data Sciences and Analytics Center, Kohli Center on Intelligent Systems, International Institute of Information Technology at Hyderabad, Hyderabad, India;Kitsuregawa Laboratory, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan;

关键词: Data mining;    weighted frequent itemset;    pattern-growth technique;    spatiotemporal database;   

DOI  :  10.1109/ACCESS.2020.2970181

来源: DOAJ

【 摘 要 】

Weighted Frequent Itemset (WFI) mining is an important model in data mining. It aims to discover all itemsets whose weighted sum in a transactional database is no less than the user-specified threshold value. Most previous works focused on finding WFIs in a transactional database and did not recognize the spatiotemporal characteristics of an item within the data. This paper proposes a more flexible model of Weighted Frequent Neighborhood Itemsets (WFNI) that may exist in a spatiotemporal database. The recommended patterns may be found very useful in many real-world applications. For instance, an WFNI generated from an air pollution database indicates a geographical region where people have been exposed to high levels of an air pollutant, say PM2.5. The generated WFNIs do not satisfy the anti-monotonic property. Two new measures have been presented to effectively reduce the search space and the computational cost of finding the desired patterns. A pattern-growth algorithm, called Spatial Weighted Frequent Pattern-growth, has also been presented to find all WFNIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also describe a case study in which our model has been used to find useful information in air pollution database.

【 授权许可】

Unknown