Evaluating the Performance of Hierarchical Clustering algorithms to Detect Spatio-Temporal Crime Hot-Spots

Baqir, Anees; Rehman, Sami ul; Malik, Sayyam; Mustafa, Faizan ul; Usman, Abdulmumini

doi:10.1109/icomet48670.2020.9074125

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…Robustness: Hierarchical clustering is robust to noise and outliers in the data, meaning that it can still identify meaningful clusters even if some data points are not wellbehaved. The steps of the complete linkage method are as follows [37]:…”

Section: Hierarchical Clustering Using Complete Linkagementioning

confidence: 99%

Analysis of Regional Potential in Merauke Regency Based on Superior Livestock Population Using a Hybrid Algorithm

Sumaryanti,

Nurcholis,

Muchlis

2024

MMEP

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Merauke Regency is the largest area in Papua Province and includes potential in the livestock sector. Regional potential analysis based on leading livestock population aims to provide regional information based on livestock sector potential, which can be used as information in policy making in government programs. A hybrid algorithm combining LQ and complete linkage can map potential livestock areas based on leading populations. The results of the LQ analysis show that there are six leading types of livestock: cows, buffaloes, horses, kampong chickens, laying chickens, and ducks. The leading livestock types can be used as a source of information regarding regional potential in the livestock business and classified into four clusters. The clustering of regional potential using a complete linkage hierarchical algorithm with a livestock population dataset by conducting four trials and yielding information that Semangga and Tanah Miring sub-districts have potential in the livestock sector. The proposed method used a hybrid approach to analyze the potential of livestock areas in Merauke and determine the leading types of livestock in the area to classify areas in each cluster and map the potential of livestock areas using GIS techniques.

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Section: Hierarchical Clustering Using Complete Linkagementioning

confidence: 99%

Analysis of Regional Potential in Merauke Regency Based on Superior Livestock Population Using a Hybrid Algorithm

Sumaryanti,

Nurcholis,

Muchlis

2024

MMEP

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“…Several attempts have been reported in the literature on the significance of statistical and deep learning approaches for prediction [17,27,35]. Particularly time series analysis techniques such as ARIMA, SARIMA [21], Exponential Smoothing (ES), and Moving averages models [36].…”

Section: Deep Learning and Statistical Techniquesmentioning

confidence: 99%

Leveraging transfer learning with deep learning for crime prediction

Butt,

Letchmunan,

Hassan

et al. 2024

PLoS ONE

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Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime.

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“…Anees Baqir et al [17] Presented A comparative study of two hierarchical clustering algorithms are Density-based spatial clustering of applications with noise (DBSCAN) and hierarchical agglomerative clustering (HAC) to determine crime hotspots in major cities. The algorithms have been exercised to the New York City(NYC) data, and this data can be used to take necessary steps to efficiently police and use manpower to counter these incidents.…”

Section: Literature Surveymentioning

confidence: 99%

Survey on Crime Analysis Using Data Mining Based on Mobile Platforms

Hussain

Aljuboori

2021

J. Al-Qadisiyah Comp. Sci. Math.

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With a large rise in crime globally, there is a necessity to analysis crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. Crime analysis and spatial analysis using Geographic Information System(GIS) tools such as hot spot generation, zoning, navigation and crime profiling, detection of mobile locations and various web-based applications are well known and can be used scientifically for citizens advancement, while crime prediction and control can be used effectively. Crime data analysts will enable the law enforcement officials to speed up the crime resolution process. We can analyze previously unknown, helpful information from unstructured data by using the theory of data mining. The purpose of this work is to perform a survey on the crime analysis by using several data mining techniques that has been applied towards criminal activities over the years to be a valuable guide to young researchers that are interested in this field.

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Evaluating the Performance of Hierarchical Clustering algorithms to Detect Spatio-Temporal Crime Hot-Spots

Cited by 9 publications

References 30 publications

Analysis of Regional Potential in Merauke Regency Based on Superior Livestock Population Using a Hybrid Algorithm

Analysis of Regional Potential in Merauke Regency Based on Superior Livestock Population Using a Hybrid Algorithm

Leveraging transfer learning with deep learning for crime prediction

Survey on Crime Analysis Using Data Mining Based on Mobile Platforms

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