Knowledge Discovery Management

Introduction to the Knowledge Discovery Process

Motivation

* Technological progress

* Too much data

* Traditional data-analysis needs evolution, automation needed

KDD (process)

= Knowledge Discovery in Databases is an (semi)-interactive and iterative, nontrivial process of identifying valid, new, potential usable and understandable patterns in data. Data is turned into information (and eventually knowledge) which can be used in the decision process.

* Step 1: Analysis of the problem

* Step 2: Data collection

* Step 3: Cleaning and preprocessing

* Step 4: Transformation and reduction

* Step 5: Selection of the DM application, task and technique

Market Basket Analysis: Use of association techniques to find groups of items that tend to occur together in transactions.

Customer Churning: Customer Churning is the loss of clients or customers. One of the main objectives of modeling customer churn is to determine the causal factors, so that the company can try to prevent the attrition from happening in the future.

Response Modelling: Response modelling typically takes a group of treated customers and attempts to build a predictive model that separates the likely responders from the non-responders through the use of one of a number of predictive modelling techniques. Typically this would use decision trees or regression analysis.

Cross-selling: Cross-selling is the action or practice of selling among or between established clients, markets, traders, etc. or the action or practice of selling an additional product or service to an existing customer

Customer Segmentation: Dividing a broad target market into subsets of consumers who have common needs (and/or common desires) as well as common applications for the relevant goods and services.

Credit Scoring: Credit Scoring is a numerical expression based on a statistical analysis of a person's credit files, to represent the creditworthiness of that person. Decide whether a customer is...