Skip to main content

Advertisement

SpringerLink
Log in

Feature selection generating directed rough-spanning tree for crime pattern analysis

  • Soft Computing Techniques: Applications and Challenges
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Nowadays, crime is a major threat to the society that affects the normal life of human beings all over the world. It is very important to make the world free from all aspects of crime activities. The main motivation of this work is to understand various crime patterns for avoiding and preventing the crime events to occur in future and save the world from such curse. Though research is going on for solving such problems, no work is noticed to handle the roughness or ambiguity that exists in the crime reports. The present work extracts all possible crime features from the crime reports and selects only the important features required for crime pattern analysis. For this purpose, it develops a purely supervised feature selection model integrating rough set theory and graph theory (spanning tree of a directed weighted graph). The crime reports are preprocessed, and crime features are extracted to represent each report as a feature vector (i.e., a set of distinct crime features). For crime pattern analysis, the main objective of our work, all extracted features are not necessarily essential, rather a minimal subset of relevant features are sufficient. Thus, feature selection is the main contribution in the paper that not only enhances the efficiency of subsequent mining process but also increases its correctness. The rough set theory-based relative indiscernibility relation is defined to measure the similarity between two features relative to the crime type. Based on the similarity score, a weighted and directed graph has been constructed that comprises the features as nodes and the inverse of the similarity score representing the similarity of feature v to u as the weight of the corresponding edge. Then, a minimal spanning tree (termed as rough-spanning tree) is generated using Edmond/Chu–Liu algorithm from the constructed directed graph and the importance of the nodes in the spanning tree is measured using the weights of the edges and the degrees (in-degrees and out-degrees) of the nodes in the spanning tree. Finally, a feature selection algorithm has been proposed that selects the most important node and remove it from the spanning tree iteratively until the modified graph (not necessarily a tree) becomes a null graph. The selected nodes are considered as the important feature subset sufficient for crime pattern analysis. The method is evaluated using various statistical measures and compared with related state-of-the-art methods to express its effectiveness in crime pattern analysis. The Wilcoxon rank-sum test, a popular nonparametric version of the two-sample t test, is done to express that the proposed supervised model is statistically significant.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Throughout the paper, the term feature is used instead of conditional feature.

  2. As the relative indiscernibility relation is defined on conditional features relative to decision feature, so conditional features more correlated to decision feature are selected. Thus, the conditional features independent to each other and correlated to decision feature are selected, which is the main objective of our feature selection algorithm.

References

  1. Abdi H, Williams JL (2010) Principal component analysis. WIREs Comput Stat 2(4):433–459

    Article  Google Scholar 

  2. Battiti R (1994) Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Netw 5(4):537–550

    Article  Google Scholar 

  3. Bazlamac CF, Hindi KS (2001) Minimum-weight spanning tree algorithms a survey and empirical study. Comput Oper Res 28(8):767–785

    Article  MathSciNet  Google Scholar 

  4. Blondel VD, Jean-Loup Guillaume RL, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):1–12

    Article  Google Scholar 

  5. Broutin N, Devroye L, McLeish E (2008) Note on the structure of Kruskal’s algorithm. Algorithmica 56(2):141

    Article  MathSciNet  Google Scholar 

  6. Chu YJ, Liu TH (1965) On the shortest arborescence of a directed graph. Sci Sin 14:1396–1400

    MathSciNet  MATH  Google Scholar 

  7. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Syst 1695:1–9. http://igraph.org

  8. Das AK, Sengupta S, Bhattacharyya S (2018) A group incremental feature selection for classification using rough set theory based genetic algorithm. Appl Soft Comput 65(C):400–411

    Article  Google Scholar 

  9. Das P, Das AK (2017) 8th international conference on computing, communication and networking technologies, pp 1–6

  10. Deo N (1974) Graph theory with applications to engineering and computer science. Prentice-Hall Inc, Upper Saddle River

    MATH  Google Scholar 

  11. Fazayeli F, Wang L, Mandziuk J (2008) Feature selection based on the rough set theory and expectation-maximization clustering algorithm. In: Chan C-C, Grzymala-Busse JW, Ziarko WP (eds) Rough sets and current trends in computing. Springer, Berlin, pp 272–282

    Chapter  Google Scholar 

  12. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11(1):10–18

    Article  Google Scholar 

  13. Hu XT, Lin TY, Han J (2003) A new rough sets model based on database systems. In: Wang G, Liu Q, Yao Y, Skowron A (eds) Rough sets, fuzzy sets, data mining, and granular computing. Springer, Berlin, pp 114–121

    Chapter  Google Scholar 

  14. Huda RK, Banka H (2018) Efficient feature selection and classification algorithm based on PSO and rough sets. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3317-9

    Article  Google Scholar 

  15. Jalil MMA, Ling CP, Noor NMM, Mohd F (2017a) Knowledge representation model for crime analysis. Procedia Comput Sci 116:484–491

    Article  Google Scholar 

  16. Jalil MMA, Mohd F, Noor NMM (2017b) A comparative study to evaluate filtering methods for crime data feature selection. Procedia Comput Sci 116:113–120

    Article  Google Scholar 

  17. Janeela Theresa MM, Joseph Raj V (2016) A maximum spanning tree-based dynamic fuzzy supervised neural network architecture for classification of murder cases. Soft Comput 20(6):2353–2365

    Article  Google Scholar 

  18. Edmonds J (1967) Optimum branchings. J Res Natl Bureau Stand 71:233–240

    Article  MathSciNet  Google Scholar 

  19. Keerthika T, Premalatha K (2016) Rough set reduct algorithm based feature selection for medical domain. J Chem Pharm Sci 9(2):896–902

    Google Scholar 

  20. Lehrmann A, Huber M, Polatkan AC, Pritzkau A, Nieselt K (2013) Visualizing dimensionality reduction of systems biology data. Data Min Knowl Discov 27(1):146–165

    Article  MathSciNet  Google Scholar 

  21. Loper E, Bird S (2002) NLTK: the natural language toolkit. In: Proceedings of the ACL-02 workshop on effective tools and methodologies for teaching natural language processing and computational linguistics, vol 1, pp 63–70

  22. Girvan M (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99(12):7821–7826

    Article  MathSciNet  Google Scholar 

  23. Maji P, Paul S (2011) Rough set based maximum relevance-maximum significance criterion and gene selection from microarray data. Int J Approx Reason 52(3):408–426

    Article  Google Scholar 

  24. Mikolov T, Chen K, Corrado G, Dean J (2013a) Efficient estimation of word representations in vector space. CoRR. arXiv:abs/1301.3781:1–12

  25. Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013b) Distributed representations of words and phrases and their compositionality. CoRR. arXiv:abs/1310.4546:1–9

  26. Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103(23):8577–8582

    Article  Google Scholar 

  27. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356

    Article  Google Scholar 

  28. Pawlak Z (1998) Rough set theory and its applications to data analysis. Cybern Syst 29(7):661–688

    Article  Google Scholar 

  29. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  30. Rosvall M, Axelsson D, Bergstrom CT (2009) The map equation. Eur Phys J Spec Top 178(1):13–23

    Article  Google Scholar 

  31. Sabu MK (2018) A rough set based feature selection approach for the prediction of learning disabilities. Int J Adv Comput Eng Netw 2(12):43–48

    Google Scholar 

  32. Sengupta S, Das AK (2012) Single reduct generation based on relative indiscernibility of rough set theory. Int J Soft Comput 3(1):107–119

    Article  Google Scholar 

  33. Shalabi LA (2017) Perceptions of crime behavior and relationships: rough set based approach. Int J Comput Sci Inf Secur 15(3):413–420

    Google Scholar 

  34. Singh B, Sankhwar JS, Vyas OP (2014) Optimization of feature selection method for high dimensional data using fisher score and minimum spanning tree. In: 2014 annual IEEE India conference (INDICON), pp 1–6

  35. Song Q, Ni J, Wang G (2013) A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Trans Knowl Data Eng 25(1):1–14

    Article  Google Scholar 

  36. Steven Bird EK, Loper E (2009) Natural language processing in python. O’Reilly Media, Sebastopol

    MATH  Google Scholar 

  37. JeraldBeno TR, K M (2012) Dimensionality reduction: rough set based feature reduction. Int J Sci Res Publ 2(9):1–6

    Google Scholar 

  38. Taha K, Yoo PD (2017) Using the spanning tree of a criminal network for identifying its leaders. IEEE Trans Inf Forensics Secur 12(2):445–453

    Article  Google Scholar 

  39. Weng J, Young DS (2017) Some dimension reduction strategies for the analysis of survey data. J Big Data 4(1):43

    Article  Google Scholar 

  40. Yager RR, Alajlan N (2015) Dempster-shafer belief structures for decision making under uncertainty. Knowl Based Syst 80(C):58–66

    Article  Google Scholar 

  41. Yang HH, Moody J (1999) Feature selection based on joint mutual information. In: Proceedings of international ICSC symposium on advances in intelligent data analysis, pp 22–25

  42. Alapati Yaswanth Kumar, Sindhu SSK (2015) Relevant feature selection from high-dimensional data using MST based clustering. Int J Emerg Trends Sci Technol 2(3):1997–2001

    Google Scholar 

  43. Zadeh L (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

  44. Zaher AA, Berretta R, Arefin AS, Moscato P (2015) Proceedings of the 13th Australasian data mining conference (AusDM 2015). In: FSMEC: a feature selection method based on the minimum spanning tree and evolutionary computation, pp 129–139

  45. Zhang M, Yao JT (2004) A rough sets based approach to feature selection. In: IEEE annual meeting of the fuzzy information, vol 1, pp 434–439

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 711103, India

    Priyanka Das & Asit Kumar Das

  2. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Chilakapalem, Andhra Pradesh, India

    Janmenjoy Nayak

Authors
  1. Priyanka Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asit Kumar Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Janmenjoy Nayak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Priyanka Das.

Ethics declarations

Conflict of interest

The authors declare that this manuscript has no conflict of interest with any other published source and has not been published previously (partly or in full). No data have been fabricated or manipulated to support our conclusions.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Das, P., Das, A.K. & Nayak, J. Feature selection generating directed rough-spanning tree for crime pattern analysis. Neural Comput & Applic 32, 7623–7639 (2020). https://doi.org/10.1007/s00521-018-3880-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3880-8

Keywords

Search

Navigation