Abstract
The crime rate is incrementing day by day, and there is a need to find out which regions are more crime prostrate so that compelling actions can be taken to deflate the crime rate by providing security measures in all the regions, especially in the more crime-prone regions. This paper provides a discursive survey on techniques used for crime pattern analysis. As we know that data analytics is an umbrella term covering different aspects such as data mining and formal concept analysis, we are focusing more on pattern analysis through formal concept analysis. This paper reviews the available literature related to crime pattern analysis depicting the methods used by various researchers followed by the research gaps. In addition to that, an introduction of formal concept analysis is depicted along with a table showing data of crime in India followed by discussion, conclusion, and the proposed work. This paper would be helpful for the starters who want to start research in this area.
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References
Nath SV (2006) Crime pattern detection using data mining. In: 2006 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology workshops, 2006. WI-IAT 2006 workshops. IEEE
Brantingham PL et al (2016) Crime pattern visualization
Zou C et al (2017) Mining and updating association rules based on fuzzy concept lattice. Future Gen Comput Syst
Loia V, Orciuoli F, Pedrycz W (2018) Towards a granular computing approach based on Formal Concept Analysis for discovering periodicities in data. Knowl-Based Syst 146:1–11
Malik DS, Mathew S, Mordeson JN (2018) Fuzzy incidence graphs: applications to human trafficking. Inf Sci
Broumi, Ullah K, Bakali A, Talea M, Singh PK, Mahmood T, Smarandache F, Bahnasse A, Patro SK, de Oliveira A (2018) Novel system and method for telephone network planning based on neutrosophic graph. Global J Comput Sci Technol 18(2):1–10 (Version 1.0)
Sarwar M, Akram M (2017) Novel applications of m-polar fuzzy concept lattice. New Math Nat Comput 13(03):261–287
Singh PK (2017) Three-way fuzzy concept lattice representation using neutrosophic set. Int J Mach Learn Cybern 8(1):69–79. https://doi.org/10.1007/s13042-016-0585-0 ISSN: 1868-8071 (print version)
Singh PK (2018) m-polar fuzzy graph representation of concept lattice. Eng Appl Artif Intell 67:52–62
Singh PK (2017) Concept lattice visualization of data with m-polar fuzzy attribute. Granul Comput 3(2):123–137
Singh PK, Kumar CA, Gani A (2016) A comprehensive survey on formal concept analysis, its research trends and applications. Int J Appl Math Comput Sci 26(2):495–516
Sathyadevan S, Surya Gangadharan S (2014) Crime analysis and prediction using data mining. In: 2014 first international conference on networks & soft computing (ICNSC). IEEE
Sivaranjani S, Sivakumari S, Aasha M (2016) Crime prediction and forecasting in Tamil Nadu using clustering approaches. In: International conference on emerging technological trends (ICETT). IEEE
Hamdy E et al (2015) Criminal act detection and identification model. In: 2015 seventh international conference on advanced communication and networking (ACN). IEEE
Handayanto RT et al (2012) Real-time surveillance system using pattern matching. In: 2012 sixth UKSim/AMSS European symposium on computer modeling and simulation (EMS). IEEE
Eftelioglu E et al (2016) Ring-shaped hotspot detection. IEEE Trans Knowl Data Eng 28(12):3367–3381
Sharma M (2014) Z-CRIME: a data mining tool for the detection of suspicious criminal activities based on decision tree. In: 2014 international conference on data mining and intelligent computing (ICDMIC). IEEE
Das P, Das AK (2017) Crime analysis against women from online newspaper reports and an approach to apply it in dynamic environment. In: 2017 international conference on big data analytics computational intelligence (ICBDAC). IEEE
Poelmans J et al (2011) Formally analyzing the concepts of domestic violence. Expert Syst Appl 38(4):3116–310
Akram M (2019) m-Polar fuzzy graphs. https://doi.org/10.1007/978-3-030-03751-2
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The authors would like to thank each reviewer for their constructive comments to improve the quality of paper. Same time authors thank the Amity University management for providing an infrastructure for research and innovation.
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Kapoor, P., Singh, P.K., Cherukuri, A.K. (2020). Crime Data Set Analysis Using Formal Concept Analysis (FCA): A Survey. In: Jain, V., Chaudhary, G., Taplamacioglu, M., Agarwal, M. (eds) Advances in Data Sciences, Security and Applications. Lecture Notes in Electrical Engineering, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-15-0372-6_2
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DOI: https://doi.org/10.1007/978-981-15-0372-6_2
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