Developing an Efficient Mining Framework using Dependable High Utility Pattern Mining


  • Aditya Nellutla Research Scholar, Sathyabama Institute of Science and Technology, Chennai
  • Dr. N. Srinivasan Professor, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai


Dependable Itemset Mining, Data Mining, Pattern Mining, Utility-based Patterns


One of the most active areas of study is utility mining, which has a wide range of real-world application possibilities. A utility function is used in high utility pattern mining to extract all patterns that are more useful than the minimum. It is an inherent shortcoming of these algorithms because when this threshold is set too low, a large number of patterns are formed. As a result, it may be more difficult to interpret the patterns revealed throughout the mining process, making it less efficient. In addition, many of these patterns are unreliable and difficult to use in making judgments. Adapting the notion of dependability to mine an important sort of pattern known as reliable high utility patterns, this research offered a unique challenge of mining dependable high utility patterns. An effective method known as Dependable High Utility Pattern Mining (DHUPM) is provided to solve this problem. DHUPM presents numerous ways for effectively handling reliable patterns with high utility values and introduces three new metrics for measuring the reliability of utility-based patterns. The experimental results show that up to 98.56% of the patterns found by typical high utility pattern mining methods are unreliable. In comparison, the DHUPM technique yields patterns with an average dependability proportion that is at least 43.6% greater. In addition, the proposed pruning algorithms reduce both the performance and memory consumption of the programme.


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Data Mining Process Overview




How to Cite

A. . Nellutla and D. N. . Srinivasan, “Developing an Efficient Mining Framework using Dependable High Utility Pattern Mining”, Int J Intell Syst Appl Eng, vol. 10, no. 3, pp. 86–94, Oct. 2022.



Research Article