Web Data Mining Framework for Accidents Data
Gowtham Mamidisetti1, Nalluri Gowtham2, Ramesh Makala3

1Gowtham Mamidisetti, Department of Computer Science and Engineering, Presidency University, Bangalore (Karnataka), India.
2Nalluri Gowtham, Department of Information Technology, RVR & JC College of Engineering, Guntur (Andhra Pradesh), India.
3Ramesh Makala, Department of Information Technology, RVR & JC College of Engineering, Guntur (Andhra Pradesh), India.
Manuscript received on 12 February 2019 | Revised Manuscript received on 02 March 2019 | Manuscript Published on 08 June 2019 | PP: 49-51 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E10100275S419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Identification of factors associated with large amount of data is the main key challenge in big data analysis. Heterogeneous nature of data is other factor that makes the analysis difficult. Accident occur due to various factors like poor lighting, un controllable speed at curves, hill region with unidentified climate change, fog, vehicle bad condition, driver health status. Data recorded for these above factors are considered under analysis using segmentation and clustering methods. Data analysis is done on the accident data to find differences in traffic conditions, weather conditions and road conditions. A research on reasons behind the accidents and impact of public health on accidents data is presented in this work. Segmentation of accident data is done with k-mode and associate rule mining. Trend Identification with similarity analysis approach is used in analyzing road accident data. This papers focuses on finding best analysis model for accident data analysis and also to find the combination of methods required to predict influenced factors that need to be focused to reduce impact of health care on accidents.
Keywords: K-modes; Latent Class Analysis; Association Rule Mining; Trend Analaysis.
Scope of the Article: Semantic Web