Modified GUIDE (LM) algorithm for mining maximal high utility patterns from data streams

Manike, Chiranjeevi; Om, Hari

doi:10.1080/18756891.2015.1023589

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Despite its many uses, High Utility Pattern Mining has significant drawbacks. As per result, lots of modifications of HUPM have emerge in the literature, such as Incremental Utility Mining [18], which goal to mining HUPs from active datasets, On-Shelf High Utility Pattern Mining [19], which takes into account the projection life of data, and Concise Representations of High Utility Patterns (e.g., Maximal Itemsets [20] and Closed High Utility Itemsets [21]), which needs to be extracted a small list of meaningful…”

Section: Huim Based Reviewmentioning

confidence: 99%

Graph Ontology Based IAFCM Model for Itemset Mining in Transactional Database: An Optimized Fuzzy Framework

2022

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HUIM (High Utility Itemset Mining) is one of the most analyzed data mining activities. Product suggestion, e-learning, bioinformatics, text mining, market basket analysis, and web click stream analysis are few of the areas where it can be used. Cost savings, greater competitive advantage, and increased revenue are the advantages gained by pattern analysis. However, because HUIM approaches do not examine the correlation of retrieved patterns, they may uncover false patterns. As a result, a number of technique for mining related HUIs have been presented. These algorithms still have issues with computational cost, both in conditions of period and memory usage. As a result, a method for mining weighted temporal patterns is proposed. The suggested method begins by preprocessing time series-based data into fuzzy itemsets. These are fed into the Improved Adaptive Fuzzy C Means (IAFCM) technique, which is a hybrid of the FCM clustering method and the Graph based Ant Colony Optimization (GACO) technique. The proposed IAFCM technique accomplishes two goals: IAFCM clustering and data reduction in FCM clusters, and ii) optimal itemet placement in clusters using GACO. Using GACO, the suggested technique produces high-quality clusters. On these clusters, weighted sequential pattern mining is used to find the most effective sequential patterns, which take into account knowledge of patterns with low frequency and high weight in a repository that is updated over time. The results of this method show that when compared to other traditional methodologies, the IAFCM with GACO improves execution time. Furthermore, it improves the data representation process by increasing accuracy while using less memory.

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Section: Huim Based Reviewmentioning

confidence: 99%

Graph Ontology Based IAFCM Model for Itemset Mining in Transactional Database: An Optimized Fuzzy Framework

2022

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“…As a consequence, many extensions of high utility pattern mining appeared in the literature such as Incremental Utility Mining [37]- [39] which aims to extract HUPs from dynamic databases, On-Shelf High Utility Pattern Mining [40]- [42] in which the shelf time of items is considered, Concise Representations of High Utility Patterns (e.g. Maximal itemsets [43], [44] and Closed High Utility Itemsets [45], [46]) that aim to extract a small list of meaningful HUPs.…”

Section: B High Utility Pattern Mining (Hupm)mentioning

confidence: 99%

A Survey of Correlated High Utility Pattern Mining

2021

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Pattern mining is an unsupervised data mining approach aims to find interesting patterns that can be used to support decision-making. High Utility Pattern Mining (HUPM) aims to extract patterns having high utility or importance which has broad applications in domains such as market basket analysis, product recommendation, bioinformatics, e-learning, text mining, and web click stream analysis. However, it has several limitations on real life scenarios; as a consequence, many extensions of HUPM appeared in the literature such as Correlated High Pattern Mining, Incremental Utility Mining, On-Shelf High Utility Pattern Mining, and Concise Representations of High Utility Patterns. The Correlated High Utility Pattern Mining aims to extract interesting high utility patterns by utilizing both Utility and Correlation measures. Several algorithms have been proposed to mine the correlated high utility patterns. These algorithms differ in the measures used to evaluate the interestingness of the patterns, data structures and pruning properties which they use to improve the mining performance. This paper presents a detailed survey on correlated high utility pattern mining, their methods, measures, data structures and pruning properties.

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“…Although High Utility Pattern Mining has several applications, it has some limitations. As a consequence, many extensions of High Utility Pattern Mining appeared in the literature such as Incremental Utility Mining [37,38] which aims to extract HUPs from dynamic databases, On-Shelf High Utility Pattern Mining [39][40][41] in which the shelf time of items is considered, and Concise Representations of High Utility Patterns (e.g., Maximal Itemsets [42,43] and Closed High Utility Itemsets [44][45][46][47]) that aim to extract a small list of meaningful HUPs.…”

Section: Complexitymentioning

confidence: 99%

Efficient Utility Tree‐Based Algorithm to Mine High Utility Patterns Having Strong Correlation

et al. 2021

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High Utility Itemset Mining (HUIM) is one of the most investigated tasks of data mining. It has broad applications in domains such as product recommendation, market basket analysis, e-learning, text mining, bioinformatics, and web click stream analysis. Insights from such pattern analysis provide numerous benefits, including cost cutting, improved competitive advantage, and increased revenue. However, HUIM methods may discover misleading patterns as they do not evaluate the correlation of extracted patterns. As a consequence, a number of algorithms have been proposed to mine correlated HUIs. These algorithms still suffer from the issue of the computational cost in terms of both time and memory consumption. This paper presents an algorithm, named Efficient Correlated High Utility Pattern Mining (ECoHUPM), to efficiently mine the high utility patterns having strong correlation items. A new data structure based on utility tree (UTtree) named CoUTlist is proposed to store sufficient information for mining the desired patterns. Three pruning properties are introduced to reduce the search space and improve the mining performance. Experiments on sparse, very sparse, dense, and very dense datasets indicate that the proposed ECoHUPM algorithm is efficient as compared to the state-of-the-art CoHUIM and CoHUI-Miner algorithms in terms of both time and memory consumption.

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Modified GUIDE (LM) algorithm for mining maximal high utility patterns from data streams

Cited by 7 publications

References 19 publications

Graph Ontology Based IAFCM Model for Itemset Mining in Transactional Database: An Optimized Fuzzy Framework

Graph Ontology Based IAFCM Model for Itemset Mining in Transactional Database: An Optimized Fuzzy Framework

A Survey of Correlated High Utility Pattern Mining

Efficient Utility Tree‐Based Algorithm to Mine High Utility Patterns Having Strong Correlation

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