Terrorist Attacks Analysis Using Clustering Algorithm Pranjal Gupta, A. Sai Sabitha, Tanupriya Choudhury and Abhay Bansal Abstract The factors that are affecting the world are terrorism, economy, political changes, pollution, etc. Out of which terrorism is one the biggest factor which is affecting the economy, society along with loss of precious human lives. In this paper, the data mining techniques are implemented to infer certain trends and pattern of terrorist attacks in India. K-means clustering is used to determine the year in which the terrorist groups were most active and also which terrorist group has affected the most. The experimental result is implemented in Rapidminer tool to determine the active group and the affected year. 1 Introduction Data mining is the process of extracting important knowledge from the data. It has been used in many areas such as image processing, text processing, etc. Its appli- cation includes fraud detection, attacks data analysis, etc. It involves the other processes also such as data transformation, data integration, data reduction and data cleaning. It plays a crucial role in determining the new trends through the data. Data mining are being used in analyzing and finding unknown trends or patterns of attack. Clustering technique can be used to identify the group of targeted cities of regions that are affected most. It gives the information about the group who attacked in a particular region and also tells about their active years [1]. P. Gupta (&)  A.S. Sabitha  T. Choudhury  A. Bansal Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India e-mail: pranjal.gupta2194.pg@gmail.com A.S. Sabitha e-mail: assabitha@amity.edu T. Choudhury e-mail: tchoudhury@amity.edu A. Bansal e-mail: abansal1@amity.edu © Springer Nature Singapore Pte Ltd. 2018 S.C. Satapathy et al. (eds.), Smart Computing and Informatics, Smart Innovation, Systems and Technologies 78, https://doi.org/10.1007/978-981-10-5547-8_33 317