Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Ali, Amjad; Faheem, Zaid Bin; Waseem, Muhammad; Draz, Umar; Safdar, Zanab; Hussain, Shafiq; Yaseen, Sana

doi:10.1109/inmic50486.2020.9318060

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…It is used on a data source which contains categorical data and it reduces the execution of clustering. It is applicable on any data source of any size [44].…”

Section: Cactus (Clustering Categorical Data Usingmentioning

confidence: 99%

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Performance Evaluation Of Clustering Algorithms With Constraints And Parameters

PRASAD,

2023

Preprint

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Data Extraction is a technique is called as clustering which is used to retrieve data either from the files or data bases or both. This paper focuses on the performance evaluation parameters of the clustering algorithms based on different parameters or conditions or constraints and parameters which are used to perform the clustering process to get the clusters on the data sets. Therefore best clusters are retrieved when best parameters or conditions or constraints or preferences which are applied on the data sources for the clustering process. These parameters or conditions or constraints are opted by the user called as user preferences.

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“…It is used on a data source which contains categorical data and it reduces the execution of clustering. It is applicable on any data source of any size [44].…”

Section: Cactus (Clustering Categorical Data Usingmentioning

confidence: 99%

“…It is a similar on grid clustering Technique where the dataset is recursively split into a limited number of cells. It concentrates on value space near the data points but not only on data points [58].…”

Section: Sting (Statistical Information Grid)mentioning

confidence: 99%

Performance Evaluation Of Clustering Algorithms With Constraints And Parameters

PRASAD,

2023

Preprint

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“…In this step we have applied k-means clustering technique. k-means [46][47][48] is a clustering method that divides n observations into groups so that in each group every observation is closely related. This kind of clustering is the most used because of its less complexity and efficiency.…”

Section: Clusteringmentioning

confidence: 99%

Natural Language Processing Application on Commit Messages: A Case Study on HEP Software

Yang¹,

Ronchieri²,

Canaparo³

2022

Preprint

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Version Control and Source Code Management Systems, such as GitHub, contain large amount ofunstructured historical information of software projects. Recent studies have introduced Natural Language Processing (NLP) to help software engineers retrieve information from very large collection of unstructured data. In this study, we have extended our previous study by increasing our datasets and ML and clustering techniques. Method: We have followed a complex methodology made up of various steps. Starting from the raw commit messages we have employed NLP techniques to build a structured database. We have extracted their main features and used as input of different clustering algorithms. Once labelled each entry, we have applied supervised machine learning techniques to build a prediction and classification model. Results: We have developed a machine learning-based model to automatically classify commit messages of a software project. Our model exploits a ground-truth dataset which includes commit messages obtained from various GitHub projects belonging to the HEP context. Conclusions: The contribution of this paper is two-fold: it proposes a ground-truth database; it provides a machine learning prediction model. They automatically identify the more change-proneness areas of code. Our model has obtained a very high average precision, recall and F1-score.

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“…The term "clustering" is used interchangeably to describe how search groups can collect disaggregated data. Depending on the community setting, presumptions made about the assembly process's component parts, and the context in which the assembly is employed during the data gathering process, several labels emerge [6]. In this study, clustering analysis is one approach that can be used to group OCD data into categories that have similar characteristics.…”

Section: Introductionmentioning

confidence: 99%

Comparison of K-Means, BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data

Rizalde,

Mubarak,

Ramadhan

et al. 2024

PREDATECS

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The hallmarks of Obsessive-Compulsive Disorder (OCD) are intrusive, anxiety-inducing thoughts (called obsessions) and associated repeated activities (called compulsions). To understand the patterns and relationships between OCD data that have been obtained, data will be grouped (clustering). In clustering using several clustering algorithms, namely K-Means, BIRCH, In this work, hierarchical clustering was used to identify the optimal cluster value comparison, and the Davies Bouldin Index (DBI) was used to confirm the results. Then the results of the best cluster value in processing OCD data are using the BIRCH algorithm in the K10 experiment which gets a value of 1.3. While the K-Means algorithm obtained the best cluster at K10 with a value obtained of 1.36 and the Hierarchical clustering algorithm also at the K10 value of 2.03. Thus in this study, the comparison results of the application of 3 clustering algorithms obtained results, namely the BIRCH algorithm shows the value of the resulting cluster is the best in clustering OCD data. This means that the BIRCH algorithm can be used to cluster OCD data more accurately and efficiently.

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Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Cited by 6 publications

References 20 publications

Performance Evaluation Of Clustering Algorithms With Constraints And Parameters

Performance Evaluation Of Clustering Algorithms With Constraints And Parameters

Natural Language Processing Application on Commit Messages: A Case Study on HEP Software

Comparison of K-Means, BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data

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