Introduction to Data Mining a j. m m. (ton) weijters

Introduction to Data Mining

• a.j.m.m. (ton) weijters

• (slides are partially based on an introduction of Gregory Piatetsky-Shapiro)

Overview

• Why data mining (data cascade)

• Application examples

• Data Mining & Knowledge Discovering

• Data Mining versus Process Mining

Why Data Mining

• Cascade of data

• Different growth rates, but about 30% each year is a low growth rate estimation

• The possibility to use computers to analyze data

• 1975 computer for the whole university (main frame) with 1MB working memory, now a PC with 512 MB working memory

Cascade of data

• Business and government systems (transactions system, ERP systems, Workflow systems, ...)
• Scientific data: astronomy, biology, etc
• Web, text, and e-commerce (new regularities, about data storage to prevent attempts)
• Hospitals, internal revenue service
• ...

Examples large data bases

• AT&T handles billions of calls per day

• so much data, it cannot be all stored -- analysis has to be done “on the fly”

• Europe's Very Long Baseline Interferometry (VLBI) has 16 telescopes, each of which produces 1 Gigabit/second of astronomical data over a 25-day observation session

• Google

First conclusion

• Very little data will ever be looked at by a human

• Data Mining algorithms and computers are NEEDED to make sense and use of data.

Application examples I

• Customer Relationship Management (CRM)

• Based on a data base with client information and behavior try to select other potential consumers of a product.
• Euro miles.

• Profiling tax cheaters

• Based on the profile of the tax payer and some figures from the tax (electronic) form try to product tax cheating.

Application examples II

• Health care

• Given the patient profile and the diagnoses try to predict the number of hospital days. Information is used in planning system.

• Industry

• Job shop planning. Based on already accepted jobs, try to product the delivery time of a new offered job.

Type of applications

• Classification (supervised)

• Credit risk: result of data mining are rules that can be used to classify new clients as: high, normal, low

• Estimation (supervised)

• Credit risk: output is not a classification but a number between -1 and 1 to indicate risk (-1.0 very low, 0.0 normal, +1.0 very high)

• Clustering (unsupervised)

• Associations: e.g. Bier & Chips & Peanuts occur frequently in a shopping list of one person

• Visualization: to facilitate human discovery

Supervised verses unsupervised

• Supervised (Credit risk)

• Starting point is a historical data base with client information and his/her financial data including credit history (classification). This data base is used to induce credit risk rules.

• Unsupervised (Clustering)

• Try to cluster customers into similar groups (how many groups, in which sense similar)

Other related fields

• Data warehouse

• A data warehouse thus not contain simply accumulated data at a central point, but the data is carefully assembled from a variety of information sources around the organization, cleaned u, quality assured, and then released (published).

• Business Intelligence (BI)

• The use of data in the data ware house to support the managers with important information

Data Mining versus Process Mining

• Process Mining is data mining but with a strong business process view.

• Some of the more traditional data mining techniques can be used in the context of process mining.

• Some new techniques are developed to perform process mining (mining of process models).

Why Process Mining

• Traditional As-Is analysis of business processes strongly based on the opinion of process expert. The basic idea is to assemble an appropriate team and to organize modeling sessions in which the knowledge of the team members is used to build an adequate As-Is process model.

• The surplus values of process mining in the As-Is analysis are:

• information based on the real performance of the process (objective)
• more details