What is Data Mining?

• Data mining or the process of extracting knowledge from data, is the heart of the data analysis process.

• Its goal is to identify correlations in data, find patterns and variations.

• Pattern recognition is the discovery of regularity's or commonality's in data.

• When we analyze this data to gain insights into the habits or behaviors of users, for example, the time of the day when maximum users tend to login or user roles that typically spend the maximum hours logged into the application or modules in the workflow application that are being used where examining the data manually or through tools to uncover patterns hidden in the data.

• Clustering is similar to classification, but involves grouping data into clusters so they can be treated as groups.

• Association rule mining (연관규칙 마이닝) is a technique that helps establish our relationship between two data events. For example, the purchase of a laptop being frequently accompanied by the purchase of a cooling pad.

• Sequential patterns (순서적인 패턴찾기) is the technique that traces a series of events that take place in a sequence. For example, tracing a customer shopping trail from the time they log into an online store to the time they log out.

• Affinity grouping (선호도를 기반으로 그룹화 하기) is a technique used to discover Co occurrence in relationships.

Tools for Data Mining

• R is one of the most widely used languages for performing statistical modeling and computations by statisticians and data miners. R is packaged with hundreds of libraries explicitly built for data mining operations such as regression, classification, data clustering, association rule mining, text mining, outlier detection, and social network analysis.

• SPSS stands for Statistical Process for Social Sciences. While the name suggests its original usage in the field of Social Sciences, it is popularly used for advanced analytics, text analytics, trend analysis, validation of assumptions, and translation of business problems into data science solutions

• Watson Studio enables team members to collaborate on projects, that can range from simple exploratory analysis to building machine learning and AI models.

• SAS Enterprise Miner is a comprehensive, graphical workbench for data mining. It provides powerful capabilities for interactive data exploration, which enables users to identify relationships within data. SAS can manage information from various sources, mine and transform data, and analyze statistics.