A Generalized Study on Data Mining and Clustering Algorithms

Ahmed, Syed Thouheed; Kumar, Sushant; Anusha, B.; P, Bhumika; M, Gunashree; Ishwarya, B.

doi:10.1007/978-3-030-41862-5_114

Cited by 11 publications

(8 citation statements)

References 5 publications

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“…This method divides the space of objects into a fixed number of cells, or a grid, on which the clustering tasks are carried out [5]. It's based on a method of answering queries in multi-level grid layouts that emphasizes clustering.…”

Section: Grid Based Method: -mentioning

confidence: 99%

Study of Clustering Data Mining Techniques

Dinesh Bhardwaj,

2023

IJRITCC

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Data mining's primary purpose is to take a massive records series and wreck it down right into a more plausible form for evaluation and alertness. Exploratory facts evaluation and information mining applications frequently center on clustering. The time period "clustering" refers back to the method of categorizing facts factors into groupings wherein the objects within every cluster have more similarities than differences (clusters). Each approach serves a completely unique motive, determined by using the nature of the records at hand and the needs of the software. Nonetheless, our research has led us to the realization that the K-way approach outperforms the options in a huge type of settings. In this look at, senior undergraduate and master's degree college students from the Faculty of Economics and Business Administration at Babe?-Bolyai University of Cluj-Napoca participated via the usage of questionnaires in a collaborative effort, with the gathered data being processed through information mining clustering techniques, graphical and percent representations, the use of algorithms applied in the software program Weka

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Section: Grid Based Method: -mentioning

confidence: 99%

Study of Clustering Data Mining Techniques

Dinesh Bhardwaj,

2023

IJRITCC

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“…Unlike pre-existing models, a CNN model is designed from scratch and trained using image samples acquired specifically for the study's context. This customized model is built to distinguish between two classes - [18] the presence or absence of potholes in the given images.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Version of Seeding and Constraint Support in Ipog Strategy for Variable Strength Interaction T-Way Testing

(Corresponding Author),

Sobri Hashim,

Maiwada

et al. 2023

MJCS

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Interaction strength testing (known as t-way testing) is one of the effective software testing techniques used to find problems with how system components interact with one another. The emergence of variable strength interaction occurs because of the covering array's identified limitation, which prevents it from handling varying parameter testing strengths. Several works were formed in t-way testing to generate a small test suite size using either One-Parameter-at-a-Time (OPAT) or One-Test-at-a-Time (OTAT) approach and base on interaction strength that include uniform, variable and/or input-output relation interaction, since t-way testing is highly complicated (NP-hard). However, no single OPAT t-way strategy claims to support seeding and constraint in variable strength interaction. Motivated by these challenges, the paper presents an enhanced version of IPOG strategy for t-way testing with seeding, constraint, and variable strength interaction supports, named SCIPOG-VS. To assess its efficiency, experiments were performed and compared to other strategies supporting variable strength interaction. Experimental results demonstrated that SCIPOG-VS is competitive in terms of test suite size, and execution time against other existing strategies significantly. We statistically tested our findings with a non-parametric Wilcoxon Signed Rank test, despite this, the outcome shows a significant difference in the size of the test suite between the various strategies.

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“…. , d. Ahmed et al [30] have presented automatic segmentation and detection of brain tumor is a notoriously complicated issue in magnetic methods are limited for detection of tumor in multimodal brain MRI. This work analyses the segmentation performance of existing state of art method improved Fuzzy C-Means clustering (FCMC) method and marker-controlled watershed method to carry out accurate brain tumor detection and enhance the segmentation results.…”

Section: Complexity Analysismentioning

confidence: 99%

Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

Kumar¹,

Ahmed²,

Xin³

et al. 2022

Computers, Materials &Amp; Continua

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This paper presents, a new approach of Medical Image Pixels Clustering (MIPC), aims to trace the dissimilar patterns over the Magnetic Resonance (MR) image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment, pattern predication and deeper investigation. The proposed MIPC consists of two stages: clustering and validation. In the clustering stage, the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering (iLIAC), Dynamic Automatic Agglomerative Clustering (DAAC) and Optimum N-Means (ONM). In the second stage, the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure (SICM). Experimental results show that the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.

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A Generalized Study on Data Mining and Clustering Algorithms

Cited by 11 publications

References 5 publications

Study of Clustering Data Mining Techniques

Study of Clustering Data Mining Techniques

Enhanced Version of Seeding and Constraint Support in Ipog Strategy for Variable Strength Interaction T-Way Testing

Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

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