Web Search Result Clustering using Heuristic Search and Latent Semantic Indexing

Alam, Mansaf; Sadaf, Kishwar

doi:10.5120/6342-8633

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…They apply beam search in the search result graph in parallel to traditional topical clustering method on the clusters so formed. In [23], authors propose an approach based on the topology i.e. hyperlink and contents of the documents returned by the search engine.…”

Section: Rank-based and Hybrid Search Results Clusteringmentioning

confidence: 99%

Web Search Result Clustering- A Review

Sadaf¹

2012

IJCSES

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The ever-increasing information on the web with its heterogeneity and dynamism needs an information retrieval system which serves searcher's ambiguous, ill-formed, short queries with relevant result in a precise way. Web search result clustering has been emerged as a method which overcomes these drawbacks of conventional information retrieval (IR) systems. It is the clustering of results returned by the search engines into meaningful, thematic groups. This paper gives a succinct overview and categorizes various techniques that have been used in clustering of web search results.

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Section: Rank-based and Hybrid Search Results Clusteringmentioning

confidence: 99%

Web Search Result Clustering- A Review

Sadaf¹

2012

IJCSES

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“…Traditional machine learning models includes K‐Means, k‐nearest neighbor (KNN), 27 multilayer perceptron (MLP), support vector machine (SVM), 28 naive Bayes (NB), DT, 29 and so on. As known, K‐Means and KNN are both classical clustering algorithms, and there are large number of Web‐based analysis conducted using these algorithms, such as References 30,31. However, only a few references related to webshell detection using these two clustering methods are found.…”

Section: Methodsmentioning

confidence: 99%

Ensemble machine learning approaches for webshell detection in Internet of things environments

Yong

Wei

et al. 2020

Trans Emerging Tel Tech

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The Internet of things (IoT), made up of a massive number of sensor devices interconnected, can be used for data exchange, intelligent identification, and management of interconnected "things." IoT devices are proliferating and playing a crucial role in improving the living quality and living standard of the people. However, the real IoT is more vulnerable to attack by countless cyberattacks from the Internet, which may cause privacy data leakage, data tampering and also cause significant harm to society and individuals. Network security is essential in the IoT system, and Web injection is one of the most severe security problems, especially the webshell. To develop a safe IoT system, in this article, we apply essential machine learning models to detect webshell to build secure solutions for IoT network. Future, ensemble methods including random forest (RF), extremely randomized trees (ET), and Voting are used to improve the performances of these machine learning models. We also discuss webshell detection in lightweight and heavyweight computing scenarios for different IoT environments. Extensive experiments have been conducted on these models to verify the validity of webshell intrusion. Simulation results show that RF and ET are suitable for lightweight IoT scenarios, and Voting method is effective for heavyweight IoT scenarios.

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“…The algorithm determines the frequency when two objects are clustered given two clustering solutions p1 and p2 produced by two different clustering methods. Let T be the similarity measure, as follows [ 55 ]: …”

Section: Proposed Multiview Multirepresentation Cluster Ensemble Mmentioning

confidence: 99%

Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach

et al. 2021

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Existing text clustering methods utilize only one representation at a time (single view), whereas multiple views can represent documents. The multiview multirepresentation method enhances clustering quality. Moreover, existing clustering methods that utilize more than one representation at a time (multiview) use representation with the same nature. Hence, using multiple views that represent data in a different representation with clustering methods is reasonable to create a diverse set of candidate clustering solutions. On this basis, an effective dynamic clustering method must consider combining multiple views of data including semantic view, lexical view (word weighting), and topic view as well as the number of clusters. The main goal of this study is to develop a new method that can improve the performance of web search result clustering (WSRC). An enhanced multiview multirepresentation consensus clustering ensemble (MMCC) method is proposed to create a set of diverse candidate solutions and select a high-quality overlapping cluster. The overlapping clusters are obtained from the candidate solutions created by different clustering methods. The framework to develop the proposed MMCC includes numerous stages: (1) acquiring the standard datasets (MORESQUE and Open Directory Project-239), which are used to validate search result clustering algorithms, (2) preprocessing the dataset, (3) applying multiview multirepresentation clustering models, (4) using the radius-based cluster number estimation algorithm, and (5) employing the consensus clustering ensemble method. Results show an improvement in clustering methods when multiview multirepresentation is used. More importantly, the proposed MMCC model improves the overall performance of WSRC compared with all single-view clustering models.

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Web Search Result Clustering using Heuristic Search and Latent Semantic Indexing

Cited by 7 publications

References 34 publications

Web Search Result Clustering- A Review

Web Search Result Clustering- A Review

Ensemble machine learning approaches for webshell detection in Internet of things environments

Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach

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