A hybrid Bees Swarm Optimization and Tabu Search algorithm for Association rule mining

Djenouri, Youcef; Drias, Habiba; Chemchem, Amine

doi:10.1109/nabic.2013.6617849

Cited by 32 publications

(21 citation statements)

References 16 publications

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“…Djenouri et al () approach's experimental results indicate that it has better fitness value compared to approaches in Djenouri et al () and Yan et al (). Moreover, BSO‐ARM produces more complete rules than ARMGA (Yan et al, ).…”

Section: Comparisonmentioning

confidence: 98%

A survey on association rules mining using heuristics

Ghafari

Tjortjis

2019

WIREs Data Min & Knowl

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Association rule mining (ARM) is a commonly encountred data mining method. There are many approaches to mining frequent rules and patterns from a database and one among them is heuristics. Many heuristic approaches have been proposed but, to the best of our knowledge, there is no comprehensive literature review on such approaches, yet with only a limited attempt. This gap needs to be filled. This paper reviews heuristic approaches to ARM and points out their most significant strengths and weaknesses. We propose eight performance metrics, such as execution time, memory consumption, completeness, and interestingness, we compare approaches against these performance metrics and discuss our findings. For instance, comparison results indicate that SRmining, PMES, Ant‐ARM, and MDS‐H are the fastest heuristic ARM algorithms. HSBO‐TS is the most complete one, while SRmining and ACS require only one database scan. In addition, we propose a parameter, named GT‐Rank for ranking heuristic ARM approaches, and based on that, ARMGA, ASC, and Kua emerge as the best approaches. We also consider ARM algorithms and their characteristics as transactions and items in a transactional database, respectively, and generate association rules that indicate research trends in this area. This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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Section: Comparisonmentioning

confidence: 98%

A survey on association rules mining using heuristics

Ghafari

Tjortjis

2019

WIREs Data Min & Knowl

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“…These improvements yield quality to the rules extracted by BSO-ARM, but unfortunately, the algorithm takes more CPU time. The same authors present another Hybrid approach called (HBSO-TS) in [10] for mining association rules based on Bees swarm and Tabusearch algorithms. The results show that HBSO-TS extracts useful rules in reasonable time.…”

Section: Related Work On Armmentioning

confidence: 99%

“…• Bees swarm optimization algorithm for ARM, i.e., BSO-ARM[11] • Hybrid Bees swarm optimization with tabu-search algorithm for ARM, i.e., HBSO-TS[10] • Ant colony optimization based Algorithm for ARM, i.e.,ACO R[34] • Grammar guided genetic programming for ARM, i.e., G3PARM[33] • Binary and continues gravitational search algorithm for ARM, i.e., ARMBGSA[28] …”

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confidence: 99%

Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies

Heraguemi

Kamel

Drias³

2016

Appl Intell

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Association Rule Mining (ARM) can be considered as a combinatorial problem with the purpose of extracting the correlations between items in sizeable datasets. The numerous polynomial exact algorithms already proposed for ARM are unadapted for large databases and especially for those existing on the web. Assuming that datasets are a large space search, intelligent algorithms was used to found high quality rules and solve ARM issue. This paper deals with a cooperative multi-swarm bat algorithm for association rule mining. It is based on the bat-inspired algorithm adapted to rule discovering problem (BAT-ARM). This latter suffers from absence of communication between bats in the population which lessen the exploration of search space. However, it has a powerful rule generation process which leads to perfect local search. Therefore, to maintain a good trade-off between diversification and intensification, in our proposed approach, we introduce cooperative strategies between the swarms that already proved their efficiency in multi-swarm optimization algorithm(Ring, Master-slave). Furthermore, we innovate a new topology called Hybrid that merges Ring strategy with Master-slave plan previously developed in our earlier work [23]. A series of experiments are carried out on nine well known datasets in ARM field and the performance of proposed approach are evaluated and compared with those of other recently published methods. The results show a clear superiority of our proposal against its similar approaches in terms of time and rule quality. The analysis also shows a competitive outcomes in terms of quality in-face-of multi-objective optimization methods.

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“…The first algorithm evaluates each association rule with single-objective function to evaluate each individual separately, then the second algorithm considers simultaneously several objectives to evaluate individuals' fitness discovery of rare association rules. Authors in [27] proposed a new hybrid algorithm called (HBSO-TS) for association rule mining based on hybrid method based on Bees Swarm Optimisation (BSO) and Tabu Search (TS). BSO is used to explore the search space so that it can cover most of its neighbours.…”

Section: Arm With Metaheuristicsmentioning

confidence: 99%

“…Table 4 describes the size of the used data sets, the number of transactions and the number of items in each of the transactions. We have compared the performance of the Pe-ARM with a set of well known association rule mining algorithms (BSO-ARM [27], ACO R [13], SA [40], G3PARM [11], ARMBGSA [25]). The parameters used by these algorithms are the optimal values proposed by the authors.…”

Section: Evaluation With Standard Data Setsmentioning

confidence: 99%

Penguins Search Optimisation Algorithm for Association Rules Mining

Gheraibia¹

2016

CIT. J. comput. inf. technol.

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Association Rules Mining (ARM) is one of the most popular and well-known approaches for the decisionmaking process. All classic exhaustive ARM algorithms are time consuming and generate a very large number of association rules, even the recent proposed meta-heuristics based methods generate a small number of high quality rules but with high overlapping. To deal with this issue, we propose a new ARM approach based on penguins search optimisation algorithm (Pe-ARM for short). Moreover, an efficient measure is incorporated into the main process to evaluate the amount of overlapping among the generated rules. The proposed approach also ensures a good diversification over the whole solutions space. To demonstrate the effectiveness of the proposed approach, several experiments have been carried out on different data sets and specifically on the biological ones. The results reveal that the proposed approach outperforms the well known meta-heuristics ARM algorithms in both execution time and solution quality.

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A hybrid Bees Swarm Optimization and Tabu Search algorithm for Association rule mining

Cited by 32 publications

References 16 publications

A survey on association rules mining using heuristics

A survey on association rules mining using heuristics

Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies

Penguins Search Optimisation Algorithm for Association Rules Mining

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