Presented by:
M.GURUNATH
G.SIVACHANDRA

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ABSTRACT
Almost, each and every real time process is being automated in today’s competitive world of technological advancements. Automation has become the Blood Line of life. Data Mining is one of the powerful automation tools, as it has evolved from the concept of Knowledge Discovery Knowledge Discovery is an intelligent process and Data Mining does it artificially, thus being Artificially Intelligent. Extracting data from large databases through pruning and other implicit means is not a single handed job. Data Mining has been a ‘Boon from Mars’ to many technical fields. Management fields are also not immune to it. Customer Relationship Management is one such field which has been under focus for deployment of Data Mining. Many financial companies and business organizations
have grown from rats to riches by tackling marketing issues through Data Mining.However small organizations can only dream to incorporate mining into their business as they can not afford for such softwares. CRM is a soft issue and doesn’t need complex mining softwares based on Bayesian curves and Normalized distributions. The key aspect is, it requires simplicity and not a higher level of accuracy.We have taken efforts to produce a rather simpler algorithm based on Probabilistic Classification to generate a Decision Tree. Traversing through the Decision
Tree we can predict values for unknown attributes. Tree generation requires training thealgorithm through numerous samples. Increasing the number of samples will automatically enhance the accuracy of the algorithm.Our Algorithm would bridge the gap between such small Marketing organizations and the technology of Data Mining.
FOUNDATION
• Data Mining
Data Mining is a tool that automates the detection of relevant patterns in a Database. It is a technology, which on its progressive path leads to Knowledge Discovery, thereby making the system Artificially Intelligent. In practical terms, the system is made self reliable. There are certain prerequisites needed to perform datamining. A database full of statistical data, and certain efficient pruning algorithms to mine out them, form the core region.William Frawley and Gregory Shapiro (MIT Press, 1991) defined it as “…the nontrivial extraction of implicit, previously unknown and potentially useful information from data…”In other words it is the process of discovering meaningful correlations and hidden patterns by mining large amounts of data stored in warehouses (large repository of data).
The major advantage is its capability to build predictive models rather than being retrospective.Thus data mining is about exploration and analysis, by automatic or semiautomatic means, quantities of data can help to uncover meaningful patterns and rules.
Requirements
Often people ask this question stating that statistics are enough to get knowledge, `based on previously existing data, and what is all new about data mining?!! Now let us explain you… Data mining effectively automates the statistical
process leading to more accuracy and reduction of burden. Intelligent systems learn from events i.e. discover knowledge and act more relevantly in future. Thus data analysis techniques through data mining seem to be automated self decisive tests which result in the most appropriate or rather best suited solutions to the various situations. So we use data mining as the tool to churn out data for identification from voluminous samples and action determination based on rules obtained from Knowledge
Discovery.
Applications
Data mining is not restricted towards any field. In fact it has now become an integral part of every database oriented application. However the following fields have surprisingly gained more from the tool
•Marketing
•E-commerce.
•Medicine.
•Telecommunications.
•Transportation.
•Research
•Law and order
The Process
Data mining uses simple tools to perform the churning process from large ocean of data. The following are performed:-
•Discovering knowledge
Segmentation
Classification
Association
Preferencing
Visualizing data
Data mining softwares
There are many leading vendors who provide data mining solutions.
To name a few:-
Clementine from SPSS Inc, Chicago, Illinois, Darwin from Oracle, Decision series from NeoVista, Enterprise Miner from SAS, Intelligent Miner from IBM, Knowledge SEEKER and Knowledge Studio from Angoss.The above softwares are deployed by various global firms and government organizations to gain performance and advancements in perspective fields.
Customer RelationshipManagement.(CRM)-A core marketing issue. Any private firm or organization generally spends more money to get a new customer than to retain the existing customer, but it is far more expensive to win back a customer after they have left, than it is to keep satisfied in the first place.So it is very much essential for a concern to maintain good relations with its customers and to keep them satisfied in all possible means for which the company might be even digging its treasury.Financial expenditures are to be effectively handled as cash is the prime commodity and thus you need certain marketing strategies to manage cash. These issues are addressed as CRM issues. In order to stay competitive companies develop strategies to become customer focused, customer-driven, and customer–centric. All these terms define the companies’ desire to build lasting customer relationships. CRM is viewed as solution that makes these efforts valuable to the company and the customers alike.
Data mining & CRM issues-Their Inter Relation
As data mining is about exploration and analysis, by automatic or semiautomatic means, quantities of data can help to uncover meaningful patterns and rules. These patterns and rules help corporations improve their marketing, sales and customer support operations to better understand their customers. Over the years, corporations have ccumulated very large databases from applications such as Enterprise Resource Planning (ERP), Client Relationship Management (CRM), or other operational systems. Our paper would deal with implementing data mining algorithm for solving a typical CRM problem.
Decision Trees
The decision tree is probably the most popular technique for predictive modeling. An example explains some of the basics of the decision tree algorithm. The following table shows a set of training data that could be used to predict
credit risk. In this example, fictionalized information was generated on customers that included their debt level, income level, what type of employment they had and whether they were at a good or bad credit risk.