TY  - JOUR
TI  - A Study on Data Mining : Frequent Itemset Mining Methods Apriori, FP growth, Eclat

AU  - Dhinakaran D
AU  -  Dr. Joe Prathap P M
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2017/12/31
PY  - 2017
DO  - https://doi.org/10.32628/IJSRCSEIT
UR  - https://ijsrcseit.com/CSEIT1726165
VL  - 2
IS  - 6
SP  - 526	
EP  - 533
AB  - Data mining is described as a process of discovering useful and interesting patterns hidden in huge amounts of data stored in multiple data sources. Data mining is a interdisciplinary field, ranging from Statistics, Database technology, Information recovery, Artificial intelligence, Machine learning, Pattern recognition, Neural networks, Knowledge-based systems, High-performance computing, and Data visualization have had impacts on the growth of data mining. Association rule mining is the core process in the field of data mining. It discover set of frequent items & generates ruleset within huge transaction databases. Data mining and its techniques can be enormously helpful in many fields such as business, education, government, fraud detection, and financial banking, future healthcare and so on. Data mining have a lot of merits but still data mining systems face lot of troubles and hazards. The purpose of this paper is to discuss the basic concepts of data mining, its various techniques , specifically about Frequent Itemset Mining Methods, various challenges, applications and important issues related to data mining.