Name
Title | ||
|---|---|---|
Discovering Top-k Spatial High Utility Itemsets in Very Large Quantitative Spatiotemporal databases |
Abstract | ||
|---|---|---|
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 the successful industrial application of this technique have been hindered by the following two limitations:... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/BigData52589.2021.9671912 | 2021 IEEE International Conference on Big Data (Big Data) |
Keywords | DocType | ISBN |
Runtime,Itemsets,Heuristic algorithms,Conferences,Memory management,Big Data,Knowledge discovery | Conference | 978-1-6654-3902-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pradeep Pallikila | 1 | 0 | 0.34 |
| P. Veena | 2 | 0 | 0.34 |
| R. Uday Kiran | 3 | 0 | 0.68 |
| Ram Avatar | 4 | 0 | 0.34 |
| Sadanori Ito | 5 | 0 | 0.34 |
| Koji Zettsu | 6 | 0 | 1.69 |
| P. Krishna Reddy | 7 | 0 | 0.34 |