Customer Relationship Management using Apriori Algorithm and Frequent Item Sets in Data Mining | Original Article

ABSTRACT:

Data mining (DM) is method of discovering exciting pattern information via huge quantity of data and also known as knowledge discovery from data. In this we start via investigate information in pattern extraction via Customer Relationship Management (CRM) datasets. Association rule mining (ARM) has been considered broadly in Knowledge Discovery in Databases (KDD) field for pattern extraction (PE) there exist several known algorithms to execute. The support confidence thresholds are normally utilized to direct search for exciting patterns. KDD from databases includes usage of different methods and algorithms such as Association rules, Predictions, Decision trees, Artificial intelligence. Advanced data mining techniques are used to discover relationships and other hidden patterns. ARM is among essential DM applications. From our literature survey, I observed that nearly all of pattern mining system is extensive few practical issues may happen while amount of things in every record are very huge. DM is termed as Nontrivial pulling out of implicit, formerly unknown potentially helpful information from data science of extracting useful information from wide data sets or databases. It is core rule of KD method that has data selection, pre-processing cleaning, transformation reduction, evaluation, visualization. In this paper we will discuss about Customer Relationship Management using Apriori Algorithm