Cluster-based information retrieval using pattern mining

Djenouri, Youcef; Belhadi, Asma; Djenouri, Djamel; Lin, Jerry Chun‐Wei

doi:10.1007/s10489-020-01922-x

Cited by 35 publications

(13 citation statements)

References 54 publications

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“…An ontology-based dynamic information extraction framework identifies a wide range of document resources published in the scientific community and extracts the whole structural information [35][36][37][38][39][40][41]. e accuracy and scope of information extraction can be improved using an entity-relationship-based framework [42][43][44][45][46][47]. Few research works employed the term-frequency methodology for ranking the webpages [48][49][50][51][52][53][54].…”

Section: Literature Reviewmentioning

confidence: 99%

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Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization’s functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents’ relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users’ queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.

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Section: Literature Reviewmentioning

confidence: 99%

“…In the current environment, organizations store documents in Portable Document Format (PDF) form and their relevant metadata in a different storage location. The AI tools widely use the metadata for making decisions [ 42 – 47 ]. There are many techniques for retrieving a document using a query.…”

Section: Introductionmentioning

confidence: 99%

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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“…The k-means clustering method and frequent closed item set mining were combined to extract clusters of documents and find frequent terms. The clustering method and pattern mining algorithm were integrated to search for the most relevant object from a clustered set of objects [ 36 ]. The space-time series clustering methods, such as hierarchical, partitioning-based, and overlapping clustering methods were used in big urban traffic data sets [ 37 ].…”

Section: Related Workmentioning

confidence: 99%

A Belief Two-Level Weighted Clustering Method for Incomplete Pattern Based on Multiview Fusion

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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Incomplete pattern clustering is a challenging task because the unknown attributes of the missing data introduce uncertain information that affects the accuracy of the results. In addition, the clustering method based on the single view ignores the complementary information from multiple views. Therefore, a new belief two-level weighted clustering method based on multiview fusion (BTC-MV) is proposed to deal with incomplete patterns. Initially, the BTC-MV method estimates the missing data by an attribute-level weighted imputation method with k-nearest neighbor (KNN) strategy based on multiple views. The unknown attributes are replaced by the average of the KNN. Then, the clustering method based on multiple views is proposed for a complete data set with estimations; the view weights represent the reliability of the evidence from different source spaces. The membership values from multiple views, which indicate the probability of the pattern belonging to different categories, reduce the risk of misclustering. Finally, a view-level weighted fusion strategy based on the belief function theory is proposed to integrate the membership values from different source spaces, which improves the accuracy of the clustering task. To validate the performance of the BTC-MV method, extensive experiments are conducted to compare with classical methods, such as MI-KM, MI-KMVC, KNNI-FCM, and KNNI-MFCM. Results on six UCI data sets show that the error rate of the BTC-MV method is lower than that of the other methods. Therefore, it can be concluded that the BTC-MV method has superior performance in dealing with incomplete patterns.

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“…In cases, such as the evaluation of search algorithms, this is not a problem as it is not obvious to participants that anything has changed. For example, we could employ a BAI solution to quickly select the best retrieval method among a set of competing alternatives (e.g., multiple cluster-based methods [10,18,19]).…”

Section: Utility In Practicementioning

confidence: 99%

A day at the races

et al. 2021

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Two major barriers to conducting user studies are the costs involved in recruiting participants and researcher time in performing studies. Typical solutions are to study convenience samples or design studies that can be deployed on crowd-sourcing platforms. Both solutions have benefits but also drawbacks. Even in cases where these approaches make sense, it is still reasonable to ask whether we are using our resources – participants’ and our time – efficiently and whether we can do better. Typically user studies compare randomly-assigned experimental conditions, such that a uniform number of opportunities are assigned to each condition. This sampling approach, as has been demonstrated in clinical trials, is sub-optimal. The goal of many Information Retrieval (IR) user studies is to determine which strategy (e.g., behaviour or system) performs the best. In such a setup, it is not wise to waste participant and researcher time and money on conditions that are obviously inferior. In this work we explore whether Best Arm Identification (BAI) algorithms provide a natural solution to this problem. BAI methods are a class of Multi-armed Bandits (MABs) where the only goal is to output a recommended arm and the algorithms are evaluated by the average payoff of the recommended arm. Using three datasets associated with previously published IR-related user studies and a series of simulations, we test the extent to which the cost required to run user studies can be reduced by employing BAI methods. Our results suggest that some BAI instances (racing algorithms) are promising devices to reduce the cost of user studies. One of the racing algorithms studied, Hoeffding, holds particular promise. This algorithm offered consistent savings across both the real and simulated data sets and only extremely rarely returned a result inconsistent with the result of the full trial. We believe the results can have an important impact on the way research is performed in this field. The results show that the conditions assigned to participants could be dynamically changed, automatically, to make efficient use of participant and experimenter time.

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Cluster-based information retrieval using pattern mining

Cited by 35 publications

References 54 publications

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

A Belief Two-Level Weighted Clustering Method for Incomplete Pattern Based on Multiview Fusion

A day at the races

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