Mining
 
 

Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data (KDD). Oracle Data Miner creates predictive models that application developers can integrate into applications to automate the discovery and distribution of new business intelligence-predictions, patterns and discoveries—throughout the enterprise.

 

Now, let’s talk about Oracle Data Mining (ODM). It is a constituent of Oracle Advanced Analytics Database Option and it facilitates the organisation implementing ODM to build and apply predictive models inside the Oracle Database. This action enables the organisation to understand customers better; develop customer profiles; predict their behaviour, target the best customers and reach out to many more. ODM also aids the companies to identify cross-selling opportunities; detect anomalies if any and also safeguard against potential fraud.

 

ODM also provides powerful data mining algorithms to the companies. These algorithms play very significant role in the process as these enable data analysts of every company to discover its potential insights, make more accurate predictions thereby making optimum use and leveraging its Oracle data and investment.

 

  BENEFITS OF DATA MINING:

                ·           It can predict customer behavior – Classification

·         It can predict or estimate a value –Regression

·         It can segment a population - Clustering

·         It can determine important relationships – Association

·         It can find fraudulent or “rare events” -  Anomaly Detection

 

A TYPICAL DATA MINING PROCESS INVOLVES:

 

·           Identifying the need

·           Pre-processing the input data for analysis

·           Selecting functions/Algorithms for analysis

·           Building a model

·           Testing a model and computing effectiveness

·           Applying the model to new data and predict

·           Using and refining the model