Web service discovery with incorporation of web services clustering

Jalal, Sunita; Yadav, Dharmendra Kumar; Negi, Chetan Singh

doi:10.1080/1206212x.2019.1698131

Cited by 15 publications

(1 citation statement)

References 26 publications

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“…In recent years, machine learning algorithms have played a prominent role in information discovery on the web. Clustering analysis provides some powerful modeling in text mining, document classifcation, web mining, face recognition, speech recognition, and image processing [9,10]. Undoubtedly, cluster analysis has a broad range of applications that cannot be covered in one article; however, some are highlighted here.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Changes in Clustering Solutions: A Review of Models and Applications

Atif,

Shafiq,

Farooq

et al. 2023

Journal of Probability and Statistics

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This article comprehensively reviews the applications and algorithms used for monitoring the evolution of clustering solutions in data streams. The clustering technique is an unsupervised learning problem that involves the identification of natural subgroups in a large dataset. In contrast to supervised learning models, clustering is a data mining technique that retrieves the hidden pattern in the input dataset. The clustering solution reflects the mechanism that leads to a high level of similarity between the items. A few applications include pattern recognition, knowledge discovery, and market segmentation. However, many modern-day applications generate streaming or temporal datasets over time, where the pattern is not stationary and may change over time. In the context of this article, change detection is the process of identifying differences in the cluster solutions obtained from streaming datasets at consecutive time points. In this paper, we briefly review the models/algorithms introduced in the literature to monitor clusters’ evolution in data streams. Monitoring the changes in clustering solutions in streaming datasets plays a vital role in policy-making and future prediction. Of course, it has a wide range of applications that cannot be covered in a single study, but some of the most common are highlighted in this article.

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Section: Introductionmentioning

confidence: 99%