2017 3rd International Conference on Computational Intelligence and Networks (CINE) 2017
DOI: 10.1109/cine.2017.23
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Crime Analysis Using K-Means Clustering

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Cited by 46 publications
(14 citation statements)
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“…In Vineeth et al [21], a random forest was applied on the obtained correlation between crime types to classify the state based on their crime intensity point. Unsupervised learning based methods have also been used for mining of crime patterns and crime hotpots, such as memetic differential fuzzy cluster [22] for forecasting of criminal patterns, and fuzzy C-means algorithm [23] to cluster criminal events in space. Noor et al [24] derived association mining rules to determine relationships between different crimes.…”
Section: B Crime Data Mining Visualization and Trends Forecastingmentioning
confidence: 99%
“…In Vineeth et al [21], a random forest was applied on the obtained correlation between crime types to classify the state based on their crime intensity point. Unsupervised learning based methods have also been used for mining of crime patterns and crime hotpots, such as memetic differential fuzzy cluster [22] for forecasting of criminal patterns, and fuzzy C-means algorithm [23] to cluster criminal events in space. Noor et al [24] derived association mining rules to determine relationships between different crimes.…”
Section: B Crime Data Mining Visualization and Trends Forecastingmentioning
confidence: 99%
“…They further used association rule mining to link the cause of accidents along with the identified clusters. Joshi et al [20] used K-means clustering on a dataset of crimes from New South Wales, Australia to identify cities with high crime rates. In [21], the authors used fuzzy c-means algorithm to identify potential crime locations for different cognizable crimes such as burglary, robbery and theft.…”
Section: Survey Of Trends Of Supervised and Unsupervised Machine Learning Algorithms For Crime Analysismentioning
confidence: 99%
“…To build elbow plot, k-means clustering must be run on the data set for a specific range of k-values, say k = {1,2,…n}. K-means clustering [5] is used to cluster the data set and aims to partition the given dataset into "k" partitions where each data belongs to one cluster. The data point is similar to the points of cluster, that it belongs to and is dissimilar to clusters that it does not belong to.…”
Section: Fig 7: Dendrogram For the Dataset To Find Natural Clustersmentioning
confidence: 99%