International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 4, April 2014) 314 A Comparative study of Techniques in Data Mining Manika Verma 1 , Dr. Devarshi Mehta 2 1 Asst. Professor, Department of Computer Science, Kadi Sarva Vishwavidyalaya, Gandhinagar, India 2 Associate Professor 1 GLS Institute of Computer Technology, Ahmedabad, India Abstract- Data Mining encompasses tools and technique for the “Extraction or Mining of knowledge from huge repository of data”. In Data Mining various techniques that used are Association Rule Mining, Sequential Pattern Mining, Clustering, and Classification. Varieties of algorithms are developed for each of these techniques. This paper present comparison of algorithms developed for different techniques. It also includes comparison of various algorithms available for sequential pattern mining like GSP, FreeSpan, PrefixSpan and tools available for implementation of Data Mining Algorithm. Keywords-- Data Mining, Sequential pattern mining. I. INTRODUCTION Valuable information is hidden inside the repository of data. Since, the speed at which data is generated is much faster than it can be processed and made sense, this information often remains buried and untouched. It is impossible for individuals to find valuable information hidden behind data without technological resources. Data Mining encompasses tools and technique for the “extraction or mining of knowledge from large amounts of data” (Han and Kamber, 2001). It is the process of discovering knowledge by extracting previously unknown valid actionable information or hidden patterns from large data base. The importance of data mining has been established for business applications, criminal investigations, bio- medicine, and counter-terrorism. Most retailers, for example, employ data mining practices to uncover customer buying patterns – Amazon.com uses purchase history to make product recommendations to shoppers. Data mining can be applied wherever there is an abundance of data available for and in need of analysis. Data Mining is used for extracting various interesting patterns. Example for few of the patterns are i) Along with item A, majority of time item B is also sold (Association mining) ii) After item A is sold, majority of time item B is also sold.(Sequence Pattern mining) iii) Those customer having age between 25-30, and having salary between 20,000 to 40,000 tends to buy Mobile phones ranging from 30,000 to 40,000 (Classification) iv) Students having same characteristics, like those scoring below average in Mathematics (Clustering). This paper shows the comparison between various techniques and algorithm developed for these techniques. [1][28] II. TECHNIQUES IN DATA MINING Techniques that are used in data mining describe the type of mining and data recoveryoperations. Following Tree demonstrate few key techniques and algorithm for each key technique that are used for discovering interesting patterns. [2]