Discovering Top-k Spatial High Utility Itemsets in Very Large Quantitative Spatiotemporal databases

• DOI: 10.1109/bigdata52589.2021.9671912
• Publication Date: 2022-01-13T20:39:16Z
• Authors: Pradeep Pallikila; P. Veena; R. Uday Kiran; Ram Avatar; Sadanori Ito; Koji Zettsu; P. Krishna Reddy
• Keywords: Value (mathematics)

Spatial High Utility Itemset Mining (SHUIM) is an important knowledge discovery technique with many real-world applications. It involves discovering all itemsets that satisfy the user-specified m inimum u tility (minUtil) i n a q uantitative spatiotemporal database. The popular adoption and successful industrial application of this have been hindered by following two limitations: (i) Since rationale SHUIM to find minUtil constraint, it often produces too patterns, most which may be redundant or uninteresting user. (ii) Specifying right value open research problem in SHUIM. This paper tackles these problems proposing novel model top-k spatial high utility exist A new called dynamic minimum (dMinUtil), was explored reduce search space effectively. constraint based on greedy search, where we raise its through five thresholdraising strategies. An efficient single scan algorithm employs depth-first also presented paper. Experimental results demonstrate our memory runtime efficient. We will usefulness case studies.