A Projected Upper Bound for Mining High Utility Patterns from Interval-Based Event Sequences

• URL: http://arxiv.org/abs/2212.11364v1
• Date: Wed, 21 Dec 2022 21:06:07 GMT
• Title: A Projected Upper Bound for Mining High Utility Patterns from Interval-Based Event Sequences
• Authors: S. Mohammad Mirbagheri
• Abstract summary: We propose a projected upper bound on the utility of the patterns discovered from sequences of interval-based events. Experimental results show that the new upper bound improves HUIPMiner performance in terms of both execution time and memory usage.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High utility pattern mining is an interesting yet challenging problem. The intrinsic computational cost of the problem will impose further challenges if efficiency in addition to the efficacy of a solution is sought. Recently, this problem was studied on interval-based event sequences with a constraint on the length and size of the patterns. However, the proposed solution lacks adequate efficiency. To address this issue, we propose a projected upper bound on the utility of the patterns discovered from sequences of interval-based events. To show its effectiveness, the upper bound is utilized by a pruning strategy employed by the HUIPMiner algorithm. Experimental results show that the new upper bound improves HUIPMiner performance in terms of both execution time and memory usage.

Related papers

• Quantum Algorithm Exploration using Application-Oriented Performance Benchmarks [0.0]
  The QED-C suite of Application-Oriented Benchmarks provides the ability to gauge performance characteristics of quantum computers. We investigate challenges in broadening the relevance of this benchmarking methodology to applications of greater complexity.
  arXiv  Detail & Related papers  (2024-02-14T06:55:50Z)
• Provably Efficient Learning in Partially Observable Contextual Bandit [4.910658441596583]
  We show how causal bounds can be applied to improving classical bandit algorithms. This research has the potential to enhance the performance of contextual bandit agents in real-world applications.
  arXiv  Detail & Related papers  (2023-08-07T13:24:50Z)
• HUSP-SP: Faster Utility Mining on Sequence Data [48.0426095077918]
  High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity. We design a compact structure called sequence projection (seqPro) and propose an efficient algorithm, namely discovering high-utility sequential patterns with the seqPro structure (HUSP-SP) Experimental results on both synthetic and real-life datasets show that HUSP-SP can significantly outperform the state-of-the-art algorithms in terms of running time, memory usage, search space pruning efficiency, and scalability.
  arXiv  Detail & Related papers  (2022-12-29T10:56:17Z)
• Towards Sequence Utility Maximization under Utility Occupancy Measure [53.234101208024335]
  In the database, although utility is a flexible criterion for each pattern, it is a more absolute criterion due to neglect of utility sharing. We first define utility occupancy on sequence data and raise the problem of High Utility-Occupancy Sequential Pattern Mining. An algorithm called Sequence Utility Maximization with Utility occupancy measure (SUMU) is proposed.
  arXiv  Detail & Related papers  (2022-12-20T17:28:53Z)
• Towards Target High-Utility Itemsets [2.824395407508717]
  In applied intelligence, utility-driven pattern discovery algorithms can identify insightful and useful patterns in databases. Targeted high-utility itemset mining has emerged as a key research topic. We propose THUIM (Targeted High-Utility Itemset Mining), which can quickly match high-utility itemsets during the mining process to select the targeted patterns.
  arXiv  Detail & Related papers  (2022-06-09T18:42:58Z)
• Distributed stochastic optimization with large delays [59.95552973784946]
  One of the most widely used methods for solving large-scale optimization problems is distributed asynchronous gradient descent (DASGD) We show that DASGD converges to a global optimal implementation model under same delay assumptions.
  arXiv  Detail & Related papers  (2021-07-06T21:59:49Z)
• MineReduce: an approach based on data mining for problem size reduction [58.720142291102135]
  This paper presents an approach named MineReduce, which uses mined patterns to perform problem size reduction. We present an application of MineReduce to improve a for the heterogeneous fleet vehicle routing problem.
  arXiv  Detail & Related papers  (2020-05-15T08:49:50Z)
• Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time [93.6788993843846]
  We propose a novel time-varying Bayesian optimization algorithm that can effectively handle the non-constant evaluation time. Our bound elucidates that a pattern of the evaluation time sequence can hugely affect the difficulty of the problem.
  arXiv  Detail & Related papers  (2020-03-10T13:28:33Z)
• Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining [69.00394670035747]
  Hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques.
  arXiv  Detail & Related papers  (2019-08-28T13:12:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.