An Improved K-Means with Artificial Bee Colony Algorithm for Clustering Crimes

نویسندگان

  • Mohammad Karimi Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
چکیده مقاله:

Crime detection is one of the major issues in the field of criminology. In fact, criminology includes knowing the details of a crime and its intangible relations with the offender. In spite of the enormous amount of data on offenses and offenders, and the complex and intangible semantic relationships between this information, criminology has become one of the most important areas in the field of clustering. With the development of computer systems and the development of clustering algorithms, it has been possible to interpret mass data and extract knowledge from them. There are different types of attribute in the mass data set, each of which can be suitable for crime detection. By clustering, different groups of crime can be identified and also the percentage of their occurrence. In this paper, a K-Means improved by Artificial Bee Colony (ABC) algorithm is proposed for crime clustering. In the proposed model, an ABC algorithm has been used to improve cluster centers and increase the accuracy of clustering and assignment of samples to appropriate clusters. The main motivation is to exploit the search ability of ABC algorithm and to avoid the original limitation of falling into locally optimal values of the K-Means. Evaluation has done on data set with 1994 samples and 128 features. The results show that the accuracy of the proposed model is higher than K-Means, and the Purity value of the proposed model with 500 iterations is 0.943.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Dynamic clustering with improved binary artificial bee colony algorithm

One of the most well-known binary (discrete) versions of the artificial bee colony algorithm is the similarity measure based discrete artificial bee colony, which was first proposed to deal with the uncapacited facility location (UFLP) problem. The discrete artificial bee colony simply depends on measuring the similarity between the binary vectors through Jaccard coefficient. Although it is acc...

متن کامل

Fuzzy clustering with artificial bee colony algorithm

In this work, performance of the Artificial Bee Colony Algorithm which is a recently proposed algorithm, has been tested on fuzzy clustering. We applied the Artificial Bee Colony (ABC) Algorithm fuzzy clustering to classify different data sets; Cancer, Diabetes and Heart from UCI database, a collection of classification benchmark problems. The results indicate that the performance of Artificial...

متن کامل

A Hybrid Approach for Web Document Clustering Using K-means and Artificial Bee Colony Algorithm

Nowadays data growth is directly proportional to time and it is a major challenge to store the data in an organised fashion. Document clustering is the solution for organising relevant documents together. In this paper, a web clustering algorithm namely WDC-KABC is proposed to cluster the web documents effectively. The proposed algorithm uses the features of both K-means and Artificial Bee Colo...

متن کامل

An Optimized Artificial Bee Colony Algorithm for Clustering

K-means algorithm is sensitive to initial cluster centers and its solutions are apt to be trapped in local optimums. In order to solve these problems, we propose an optimized artificial bee colony algorithm for clustering. The proposed method first obtains optimized sources by improving the selection of the initial clustering centers; then, uses a novel dynamic local optimization strategy utili...

متن کامل

OPTIMIZATION OF RC FRAMES BY AN IMPROVED ARTIFICIAL BEE COLONY ALGORITHM

A new meta-heuristic algorithm is proposed for optimal design of reinforced concrete (RC) frame structures subject to combinations of gravity and lateral static loads based on ACI 318-08 design code. In the present work, artificial bee colony algorithm (ABCA) is focused and an improved ABCA (IABCA) is proposed to achieve the optimization task. The total cost of the RC frames is minimized during...

متن کامل

An artificial bee colony approach for clustering

Clustering is a popular data analysis and data mining technique. In this paper, an artificial bee colony clustering algorithm is presented to optimally partition N objects into K clusters. The Deb’s rules are used to direct the search direction of each candidate. This algorithm has been tested on several well-known real datasets and compared with other popular heuristics algorithm in clustering...

متن کامل

منابع من

با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ذخیره در منابع من قبلا به منابع من ذحیره شده

{@ msg_add @}


عنوان ژورنال

دوره 11  شماره 3

صفحات  1- 10

تاریخ انتشار 2020-08-01

با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.

میزبانی شده توسط پلتفرم ابری doprax.com

copyright © 2015-2023