Bee swarm optimization for solving the MAXSAT problem using prior knowledge

Djenouri, Youcef; Habbas, Zineb; Djenouri, Djamel; Fournier-Viger, Philippe

doi:10.1007/s00500-017-2956-1

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…This work is a typical example of the application of pattern mining techniques to IR. The literature calls for this type of research, particularly in the era of big data because increasingly large amounts of data become available in different domains, such as constraint programming [58], business intelligence [59], computational intelligence [60], the Internet of Things (IoT), and smart environments [61]. To the best of our knowledge, this is the first work to consider the use of frequent and high-utility pattern mining in cluster-based IR when dealing with large and big collections of objects.…”

Section: Discussionmentioning

confidence: 99%

Cluster-based information retrieval using pattern mining

et al. 2020

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This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality information retrieval. For this purpose, a novel cluster-based information retrieval approach is proposed, named Cluster-based Retrieval using Pattern Mining (CRPM). This approach integrates various clustering and pattern mining algorithms. First, it generates clusters of objects that contain similar objects. Three clustering algorithms based on k-means, DBSCAN (Density-based spatial clustering of applications with noise), and Spectral are suggested to minimize the number of shared terms among the clusters of objects. Second, frequent and high-utility pattern mining algorithms are performed on each cluster to extract the pattern bases. Third, the clusters of objects are ranked for every query. In this context, two ranking strategies are proposed: i) Score Pattern Computing (SPC), which calculates a score representing the similarity between a user query and a cluster; and ii) Weighted Terms in Clusters (WTC), which calculates a weight for every term and uses the relevant terms to compute the score between a user query and each cluster. Irrelevant information derived from the pattern bases is also used to deal with unexpected user queries. To evaluate the proposed approach, extensive experiments were carried out on two use cases: the documents and tweets corpus. The results showed that the designed approach outperformed traditional and cluster-based information retrieval approaches in terms of the quality of the returned objects while being very competitive in terms of runtime.

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Section: Discussionmentioning

confidence: 99%

Cluster-based information retrieval using pattern mining

et al. 2020

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“…Data mining is used to solve the variants of realistic problems, such as Business Intelligence [6], [7], Ontology Matching [8], Constraint Programming [9], [10], and Information Retrieval [11], [12]. The data mining techniques used in information retrieval aim at discovering knowledge from a collection of documents according to a users' query.…”

Section: A Motivationmentioning

confidence: 99%

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

et al. 2020

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Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance. A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal). After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users' queries under the similarity search process. Extensive results are carried out on large and different tweet collections, and the proposed PM-HR outperforms the baseline hashtag retrieval approaches in terms of runtime, and it is very competitive in terms of accuracy. INDEX TERMS Hashtag retrieval, pattern mining, scalability.

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“…In this paper, we propose Reinforced Swarm Optimization (RSO), a novel optimization algorithm for feature selection that incorporates the features of both reinforcement learning along with swarm intelligence based BSO algorithm. BSO [5], is a metaheuristic optimization algorithm, that mimics the foraging activities of bee colony and have been used in various domains including cloud computing [18], maximum satisfiability problem (MAX-SAT) [19], document retrieval [20], parallel computing [21], biomedical image analysis [22], [23], and many more. On the other hand, reinforcement learning (RL) has been integrated into BSO to make it more adaptive and robust powered by a suitable balance between diversification and intensification of the search space, compensating the local search of the BSO search agents.…”

Section: Introductionmentioning

confidence: 99%