Rough-Set Based Hotspot Detection in Spatial Data

A special type of cluster is called hotspots in the sense that objects in the hotspot are more active as compared to all others (appearance, density, etc.). The object in a general cluster has a similarity which is less than the object in the hotspot. In spatial data mining hotspots detection is a process of identifying the region where events are more likely to happen than the others. Hotspot analysis is mainly used in the analysis of health and crime data. In this paper, the health care data set is used to find the Hotspot of the health condition in India. The clustering algorithm is used to find the hotspot. Two clustering algorithm K-medoid and Rough K-medoid are implemented to find the cluster. K-medoid is used to find the spatial cluster, Rough K-medoid finds the cluster by removing boundary points and in this way find cluster which is denser. Granules are created on the clusters created using K-medoid and Rough K-medoid and point lying in each granule is counted. Granule containing points above a particular threshold is considered as a potential hotspot. To find the footprint of the hotspot convex hull is created on each detected hotspot. Also in this paper hotspot and footprint is defined mathematically.

[1]  Jooyoung Lee,et al.  Proactive Detection of Crash Hotspots Using In-Vehicle Driving Recorder , 2016, 2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE).

[2]  Abhishek Patel,et al.  New Approach for K-mean and K-medoids Algorithm , 2012 .

[3]  Richard Weber,et al.  Evolutionary Rough k-Medoid Clustering , 2008, Trans. Rough Sets.

[4]  Firdaus,et al.  Spatio-temporal analysis of South Sumatera hotspot distribution , 2017, 2017 International Conference on Electrical Engineering and Computer Science (ICECOS).

[5]  Shashi Shekhar,et al.  Geographically Robust Hotspot Detection: A Summary of Results , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[6]  Tony H. Grubesic,et al.  On The Application of Fuzzy Clustering for Crime Hot Spot Detection , 2006 .

[7]  Ujjwal Maulik,et al.  Validity index for crisp and fuzzy clusters , 2004, Pattern Recognit..

[8]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[9]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[10]  Yuhan Dong,et al.  A novel passenger hotspots searching algorithm for taxis in urban area , 2017, 2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[11]  Saiful Akbar,et al.  Taxi passenger hotspot prediction using automatic ARIMA model , 2017, 2017 3rd International Conference on Science in Information Technology (ICSITech).

[12]  Niyati Baliyan,et al.  PAM clustering based taxi hotspot detection for informed driving , 2017, 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT).

[13]  Nicolas Hanusse,et al.  Large interactive visualization of density functions on big data infrastructure , 2015, 2015 IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV).

[14]  Martin Boldt,et al.  A Statistical Method for Detecting Significant Temporal Hotspots Using LISA Statistics , 2017, 2017 European Intelligence and Security Informatics Conference (EISIC).

[15]  Vijander Singh,et al.  Particle Swarm Optimization Using Gaussian Inertia Weight , 2007 .

[16]  Shin-ichi Minato,et al.  Evaluation of hotspot cluster detection using spatial scan statistic based on exact counting , 2019, Japanese Journal of Statistics and Data Science.

[17]  Takeo Kanade,et al.  Mode-seeking by Medoidshifts , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[18]  Witold Pedrycz,et al.  Spatiotemporal extended fuzzy C-means clustering algorithm for hotspots detection and prediction , 2017, Fuzzy Sets Syst..

[19]  K. Thangavel,et al.  Clustering Categorical Data Using Silhouette Coefficient as a Relocating Measure , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).