Data mining tools and trents

Technical paper presentaion about data mining:

Knowledge Discovery Process

The process of discovering useful knowledge from a huge data is called as Knowledge Discovery in Database (KDD) and which is often referred to as Data mining. While data mining and knowledge discovery in databases are normally treated as synonyms, but, in fact data mining is a part of knowledge discovery process.

            Data collected from multiple sources often heterogeneous is integrated into a single data storage called as target data. Data relevant to the analysis is decided on and retrieved from the data collection. Then,
it is pre-processed and transformed into an appropriate standard format. Data mining is a crucial step in which intelligent algorithm/techniques are applied to extract meaningful pattern or rules. Finally, those patterns and rules are interpreted to new or useful knowledge or information The KDD process comprises of few steps as shown in Fig. 1and explained as follows


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Manufacturing and production, marketing, health care etc., are as follows :

1) Banking: Data mining supports banking sector in the process of searching a large database to discover previously unknown patterns; automate the process of finding predictive information. Data mining helps to forecast levels of bad loans and fraudulent credit cards use, predicting credit card spending by new customers and predicting the kinds of customer best respond to new loan offered by the backs.

2) Manufacturing and production: Data mining helps to predict the machine failures and finding key factors that control optimization of manufacturing capacity.

3) Marketing: Data mining facilitates marketing sector by classifying customer demographic that can be used to predict which customer will respond to a mailing or buy a particular product and it is very much helpful in growth of business.

4) Health-Care: Data mining supports a lot in health care sector. It supports health care sector by correlating demographics of patients with critical illnesses, developing better insights on symptoms and their causes and learning how to provide proper treatments

5) Insurance: Data mining assist insurance sector in predicting fraudulent claims and medical coverage cost, classifying the important factors that affect medical coverage and predicting the customers’ pattern which customer will buy new policies.

6) Law: Law enforcement is helped by data mining by monitoring the behaviour patterns of the criminals. Tracking crime pattern, locations and criminal behaviours, identifying various attributes to data mining, assist in solving criminal cases.

7) Government and Defence: Data mining helps to forecast the cost of moving military equipment and predicting resource consumption. Apart from that it assists in testing strategies for potential military engagements and improving homeland security by mining data from many sources.