What does this research mean for the field?

Applying the Fuzzy C-Means soft clustering model to crime rate data enables the identification of overlapping regional crime patterns, facilitating the prediction of high-incidence crime areas. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The study aims to improve crime analysis and prediction using fuzzy C-means clustering techniques.

June 10, 2026ICTACT Journal on Soft ComputingOpen Access

A Violent Crime Analysis Using Fuzzy C-Means Clustering Approach

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Authors

MPM Premasundari CYC Yamini

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Overview

Randomized trial analyzes crime incidence using fuzzy clustering in U.S. states, suggesting valuable predictions.

Key Points

The study aims to improve crime analysis and prediction using fuzzy C-means clustering techniques.
Applied fuzzy C-means clustering model on the USArrests dataset.
Evaluated multiple clustering based on crime rates across various states in the U.S.
Utilized traditional clustering principles alongside overlapping clustering techniques.
Developed a model that reflects potential crime incidence across different U.S. states.
Successfully visualized crime data to aid in understanding high-risk areas.
Provided insights into how fuzzy clustering can enhance crime prediction accuracy.

Cite This Study

Premasundari et al. (2019) studied this question.

synapsesocial.com/papers/6a28fe326f82f25be989b8ca https://doi.org/10.21917/ijsc.2019.0270

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June 10, 2026ICTACT Journal on Soft ComputingOpen Access

A Violent Crime Analysis Using Fuzzy C-Means Clustering Approach

View Full Paper

Ask AI

Bookmark

Authors

MPM Premasundari CYC Yamini

Discussion

Member takes

Overview

Randomized trial analyzes crime incidence using fuzzy clustering in U.S. states, suggesting valuable predictions.

Key Points

The study aims to improve crime analysis and prediction using fuzzy C-means clustering techniques.
Applied fuzzy C-means clustering model on the USArrests dataset.
Evaluated multiple clustering based on crime rates across various states in the U.S.
Utilized traditional clustering principles alongside overlapping clustering techniques.
Developed a model that reflects potential crime incidence across different U.S. states.
Successfully visualized crime data to aid in understanding high-risk areas.
Provided insights into how fuzzy clustering can enhance crime prediction accuracy.

Cite This Study

Premasundari et al. (2019) studied this question.

synapsesocial.com/papers/6a28fe326f82f25be989b8ca https://doi.org/10.21917/ijsc.2019.0270

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