End user's view on the MiningMart project |
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Why is KDD important?Almost every company has been through a customer profiling exercise. To better target your markets and best customer prospects you need to be able to answer these proverbial questions:
Important topics in analysing dataData Mining is the process of finding new and potentially useful knowledge
from data. According to a recent study by Gartner Group, worldwide spending
on Data Mining licenses and services is expected to reach $76.3 billion
in 2005, more than tripling the $23.3 billion spend in 2000. The most
important business tasks in Data Mining are:
Data analysisOn-line Analytical Processing (OLAP) offers interactive data analysis by aggregating data and counting the frequencies. This already answers questions like the following:
Knowledge Discovery in Databases, or KDD for short, refers to the broad process of finding knowledge in data, and emphasizes the "high-level" application of particular data mining methods. The unifying goal of the KDD process is to extract knowledge from data in the context of large databases. Knowledge Discovery in Databases (KDD) can be considered a high-level query language for relational databases that aims at generating sensible reports such that a company may enhance its performance. KDD enables analysts to model virtually any customer activity and to find previously hidden patterns relevant to current business problems, or business evolution and growth. But data mining is a difficult process which requires many iterations
and adaptions in the data and in the parameter settings until a satisfactory
result is achieved. Within the data mining process considerable time is
spend for pre-processing the data (data cleaning and handling of null
values), feature generation and selection (in databases this means to
construct additional columns and select the relevant attributes). Practical
experiences have shown that the time spend on preprocessing can take from
50% up to 80% of the entire data mining process when using the traditional
attribute-value learners. That´s why preprocessing is the key issue
in data analysis. The MiningMart Approach
What is MiningMarts path to reaching the goal? Read more... Examples of successfully applied Data Mining Cases with the MiningMart SystemThe MiningMart System was successfully applied in two telecommunications companies, the National Institute of Telecommunications in Warsaw, Poland, and the Telecom Italia Lab in Alessandria, Italy. The details of these cases are published in the internet case base that MiningMart provides (see next paragraph). Case base of successful cases on the internetOne of the projects objectives is to set up a case-base of successful
cases on the internet. The shared knowledge allows all internet users
to benefit from a new case. Submitting a new case of best practice is
a safe advertisement for KDD specialists or service providers, since the
relational data model is kept private. Only the conceptual and
the case model is published. The case base can be found here. |