Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
GPT-5.1
GPT-5.1 104 tok/s
Gemini 3.0 Pro 36 tok/s Pro
Gemini 2.5 Flash 133 tok/s Pro
Kimi K2 216 tok/s Pro
Claude Sonnet 4.5 37 tok/s Pro
2000 character limit reached

Mining Weighted Sequential Patterns in Incremental Uncertain Databases (2404.00746v1)

Published 31 Mar 2024 in cs.DB and cs.AI

Abstract: Due to the rapid development of science and technology, the importance of imprecise, noisy, and uncertain data is increasing at an exponential rate. Thus, mining patterns in uncertain databases have drawn the attention of researchers. Moreover, frequent sequences of items from these databases need to be discovered for meaningful knowledge with great impact. In many real cases, weights of items and patterns are introduced to find interesting sequences as a measure of importance. Hence, a constraint of weight needs to be handled while mining sequential patterns. Besides, due to the dynamic nature of databases, mining important information has become more challenging. Instead of mining patterns from scratch after each increment, incremental mining algorithms utilize previously mined information to update the result immediately. Several algorithms exist to mine frequent patterns and weighted sequences from incremental databases. However, these algorithms are confined to mine the precise ones. Therefore, we have developed an algorithm to mine frequent sequences in an uncertain database in this work. Furthermore, we have proposed two new techniques for mining when the database is incremental. Extensive experiments have been conducted for performance evaluation. The analysis showed the efficiency of our proposed framework.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (50)
  1. Frequent pattern mining with uncertain data, in: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 29–38.
  2. Fast algorithms for mining association rules, in: Proc. 20th int. conf. very large data bases, VLDB, pp. 487–499.
  3. Mining interesting patterns from uncertain databases. Information Sciences 354, 60–85.
  4. Single-pass incremental and interactive mining for weighted frequent patterns. Expert Systems with Applications 39, 7976–7994\colorblack.
  5. An evolutionary model to mine high expected utility patterns from uncertain databases. IEEE transactions on emerging topics in computational intelligence 5, 19–28 \colorblack.
  6. Discovering high utility-occupancy patterns from uncertain data. Inf. Sci. 546, 1208–1229 \colorblack. doi:10.1016/j.ins.2020.10.001.
  7. Incremental mining of sequential patterns using prefix tree, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer. pp. 433–440.
  8. IncSpan: incremental mining of sequential patterns in large database, in: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM. pp. 527–532.
  9. Maintenance of discovered association rules in large databases: An incremental updating technique, in: Proceedings of the twelfth international conference on data engineering, IEEE. pp. 106–114\colorblack.
  10. ILUNA: single-pass incremental method for uncertain frequent pattern mining without false positives. Inf. Sci. 564, 1–26 \colorblack. doi:10.1016/j.ins.2021.02.067.
  11. A survey of sequential pattern mining. Data Science and Pattern Recognition 1, 54–77\colorblack.
  12. ProUM: Projection-based utility mining on sequence data. Inf. Sci. 513, 222–240 \colorblack. doi:10.1016/j.ins.2019.10.033.
  13. A survey of incremental high-utility itemset mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8, e1242\colorblack.
  14. Mining high-utility itemsets with both positive and negative unit profits from uncertain databases, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer. pp. 434–446\colorblack.
  15. Extracting recent weighted-based patterns from uncertain temporal databases. Engineering Applications of Artificial Intelligence 61, 161–172 \colorblack.
  16. A survey of parallel sequential pattern mining. ACM Transactions on Knowledge Discovery from Data (TKDD) 13, 1–34,\colorblack.
  17. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data mining and knowledge discovery 8, 53–87.
  18. An efficient approach for mining weighted sequential patterns in dynamic databases, in: Industrial Conference on Data Mining, Springer. pp. 215–229.
  19. Single-pass based efficient erasable pattern mining using list data structure on dynamic incremental databases. Future generation computer systems 80, 12–28\colorblack.
  20. An uncertainty-based approach: frequent itemset mining from uncertain data with different item importance. Knowledge-Based Systems 90, 239–256.
  21. Reducing the search space for big data mining for interesting patterns from uncertain data, in: 2014 IEEE International Congress on Big Data, IEEE. pp. 315–322.
  22. A tree-based approach for frequent pattern mining from uncertain data, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer. pp. 653–661.
  23. Fast tree-based mining of frequent itemsets from uncertain data, in: International Conference on Database Systems for Advanced Applications, Springer. pp. 272–287.
  24. PUF-tree: a compact tree structure for frequent pattern mining of uncertain data, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer. pp. 13–25.
  25. CanTree: a tree structure for efficient incremental mining of frequent patterns, in: Fifth IEEE International Conference on Data Mining (ICDM’05), IEEE. pp. 274–281\colorblack.
  26. Efficiently mining frequent itemsets with weight and recency constraints. Applied Intelligence 47, 769–792\colorblack.
  27. Weighted frequent itemset mining over uncertain databases. Applied Intelligence 44, 232–250.
  28. Incrementally updating the discovered sequential patterns based on pre-large concept. Intelligent Data Analysis 19, 1071–1089.
  29. High average-utility sequential pattern mining based on uncertain databases. Knowledge and Information Systems 62, 1199–1228\colorblack.
  30. Incrementally updating the high average-utility patterns with pre-large concept. Applied Intelligence 50, 3788–3807\colorblack.
  31. A project-based PMiner algorithm in uncertain databases, in: 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), IEEE. pp. 1–5\colorblack.
  32. On probabilistic models for uncertain sequential pattern mining, in: International Conference on Advanced Data Mining and Applications, Springer. pp. 60–72\colorblack.
  33. Mining sequential patterns from probabilistic databases, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer. pp. 210–221.
  34. Efficient approach for incremental weighted erasable pattern mining with list structure. Expert Systems with Applications 143, 113087\colorblack.
  35. Improvements of IncSpan: Incremental mining of sequential patterns in large database, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer. pp. 442–451.
  36. Mining sequential patterns by pattern-growth: The PrefixSpan approach. IEEE Transactions on knowledge and data engineering 16, 1424–1440.
  37. Mining weighted frequent sequences in uncertain databases. Information Sciences 479, 76–100.
  38. Tree-Miner: Mining sequential patterns from SP-Tree, in: PAKDD, Springer. pp. 44–56\colorblack.
  39. Mining sequential patterns: Generalizations and performance improvements, in: International Conference on Extending Database Technology, Springer. pp. 1–17.
  40. Uncertain-driven analytics of sequence data in IoCV environments. IEEE Trans. Intell. Transp. Syst. 22, 5403–5414 \colorblack. doi:10.1109/TITS.2020.3012387.
  41. Efficient single-pass frequent pattern mining using a prefix-tree. Information Sciences 179, 559–583\colorblack.
  42. EHAUSM: an efficient algorithm for high average utility sequence mining. Inf. Sci. 515, 302–323 \colorblack. doi:10.1016/j.ins.2019.11.018.
  43. A survey of high utility sequential pattern mining, in: High-Utility Pattern Mining. Springer, pp. 97–129\colorblack.
  44. On incremental high utility sequential pattern mining. ACM Transactions on Intelligent Systems and Technology (TIST) 9, 1–26\colorblack.
  45. Efficient mining of frequent item sets on large uncertain databases. IEEE Transactions on Knowledge and Data Engineering 24, 2170–2183.
  46. Incrementally updating the discovered high average-utility patterns with the pre-large concept. IEEE Access 8, 66788–66798\colorblack.
  47. Probabilistic convex hull queries over uncertain data. IEEE Transactions on Knowledge and Data Engineering 27, 852–865\colorblack.
  48. Efficient mining of weighted interesting patterns with a strong weight and/or support affinity. Information Sciences 177, 3477–3499.
  49. A new framework for detecting weighted sequential patterns in large sequence databases. Knowledge-Based Systems 21, 110–122.
  50. Mining probabilistically frequent sequential patterns in large uncertain databases. IEEE transactions on knowledge and data engineering 26, 1171–1184\colorblack.
Citations (19)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube