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Mining knowledge using Decision Tree Algorithm
« on: August 17, 2011, 07:10:05 am »
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Author :  Mrs. Swati .V. Kulkarni
International Journal of Scientific & Engineering Research, Volume 2, Issue 5, May-2011
ISSN 2229-5518
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Abstract— Industry is experiencing more and more competitions in recent years. The battle is over their most valuable customers. With massive industry deregulation across the world, each customer is facing an ever-growing number of choices in telecommunications and financial services. As a result, an increasing number of customers are switching from one service provider to another. This phenomenon is called customer “churning” or “attrition,” which is a major problem for these companies and makes it hard for them to stay profitable. The data sets are often cost sensitive and unbalanced. If we predict a valuable customer who will be an attritor as loyal, the cost is usually higher than the case when we classify a loyal customer as an attritor. Similarly, in direct marketing, it costs more to classify a willing customer as a reluctant one. Such information is usually given by a cost matrix, where the objective is to minimize the total cost. In addition, a CRM data set is often unbalanced; the most valuable customers who actually churn can be only a small fraction of the customers who stay. The main focus is on the output of decision tree algorithms as the input to post processing algorithms. This algorithm relies on not only a prediction, but also a probability estimation of the classification, such as the probability of being loyal. Such information is available from decision trees.
Index Terms— Attritor, Churning, CRM, cost matrix, decision tree, post processing algorithm, ROC curve. 

1   INTRODUCTION                                                                  
Extensive research in data mining[1] has been done on discovering distributional knowledge about the un-derlying data. Models such as Bayesian models, decision trees, support vector machines, and association rules have been applied to various industrial applications such as customer relationship management (CRM) [2]. Despite such phenomenal success, most of these techniques stop short of the final objective of data mining, which is to maximize the profit while reducing the costs, relying on such post processing techniques as visualization and inte-restingness ranking. While these techniques are essential to move the data mining result to the eventual applications, they nevertheless require a great deal of human manual work by experts. Often, in industrial practice, one needs to walk an extra mile to automatically extract the real “nuggets” of knowledge, the actions, in order to maximize the final objective functions. In this paper, a novel post processing technique is presented to extract actionable knowledge from decision trees. To illustrate my motivation, customer relationship management CRM is considered, in particular, the Mobile communications industry is taken as an example [3]. This industry is experiencing more and more competitions in recent years. With mas-sive industry deregulation across the world, each customer is facing an ever-growing number of choices in communications and financial services. As a result, an increasing number of customers are switching from one service provider to another. This phenomenon is called customer “churning” or “attrition,” which is a major problem for these companies and makes it hard for them to stay profitable. In addition, a CRM data set is often unbalanced; the most valuable customers who actually churn can be only a small fraction of the customers who stay. In the past, many researchers have tackled the direct marketing problem as a classification problem, where the cost-sensitive and unbalanced nature of the problem is taken into account. In management and marketing sciences, stochastic models are used to describe the response behavior of customers. In the data mining area, a main approach is to rank the customers by the estimated likelihood to respond to direct marketing actions and compare the rankings using a lift chart or the area under curve meas-ure from the ROC curve.
Like most data mining algorithms today, a common problem in current applications of data mining in intelligent CRM is that people tend to focus on, and be satisfied with, building up the models and interpreting them, but not to use them to get profit explicitly.
In this paper, a novel algorithm for post processing decision trees is presented to obtain actions that are associated with attribute-value changes, in order to maximize the profit-based objective functions. This allows a large number of candidate actions to be considered, complicating the computation. More specifically, in this paper, two broad cases are considered. One case corresponds to the unlimited resource situation, which is only an approximation to the real-world situations, although it allows a clear introduction to the problem. Another more realistic case is the limited-resource situation, where the actions must be restricted to be below a certain cost level. In both cases, the aim is to maximize the expected net profit of all the customers as well as the industry. It can be shown that finding the optimal solution for the limited resource problem and designing a greedy heuristic algorithm to solve it efficiently is computationally hard [4]. An important contribution of the paper is that it integrates data mining and decision making together, such that the discovery of actions is guided by the result of data mining algorithms (decision trees in this case)[5]. An  approach is  considered as a new step in this direction, which is to discover action sets from the attribute value changes in a non sequential data set through optimization. The rest of the paper is organized as follows:
First a base algorithm is presented for finding unre-stricted actions in Section 2. Then formulate two ver-sions of action-set extraction problems, and show that finding the optimal solution for the problems is computationally difficult in the limited resources case (Section 3). then greedy algorithms are efficient while achieving results very close to the optimal ones obtained by the exhaustive search (which is exponential in time complexity). A case study for Mobile handset manufacturing and selling company is discussed in section 4. Conclusions and future work are presented in section 5.
    This is the unlimited-resource case. The data set consists of descriptive attributes and a class attribute. For simplicity, discrete-value problem is considered in which the class values are discrete values. Some of the values under the class attribute are more desirable than others. For example, in the banking application, the loyal status of a customer “stay” is more desirable than “not stay”. The overall process of the algorithm can be briefly described in the following four steps:
1. Import customer data with data collection, data Cleaning, data preprocessing, and so on.
2. Build customer profiles using an improved decision tree learning algorithm from the training data. In this case, a decision tree is built from the training data to predict if a customer is in the desired status or not. One improvement in the decision tree building is to use the area under the curve (AUC) of the ROC curve to evaluate probability estimation (instead of the accuracy). Another improvement is to use Laplace correction to avoid extreme probability values.
3. Search for optimal actions for each customer (see Section 2 for details).
4. Produce reports for domain experts to review the actions and selectively deploy the actions.
In the next section, we will discuss the leaf-node Search algorithm used in Step 3 (search for optimal ac-tions) in detail.

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