Mining Changes for Real-Life Applications

Mining Changes for Real-Life Applications
Bing Liu, Wynne Hsu, Heng-Siew Han and Yiyuan Xia
School of Computing National University of Singapore 3 Science Drive 2 Singapore 117543 {liub, whsu, xiayy}@comp.nus.edu.sg Abstract. Much of the data mining research has been focused on devising techniques to build accurate models and to discover rules from databases. Relatively little attention has been paid to mining changes in databases collected over time. For businesses, knowing what is changing and how it has changed is of crucial importance because it allows businesses to provide the right products and services to suit the changing market needs. If undesirable changes are detected, remedial measures need to be implemented to stop or to delay such changes. In many applications, mining for changes can be more important than producing accurate models for prediction. A model, no matter how accurate, can only predict based on patterns mined in the old data. That is, a model requires a stable environment, otherwise it will cease to be accurate. However, in many business situations, constant human intervention (i.e., actions) to the environment is a fact of life. In such an environment, building a predictive model is of limited use. Change mining becomes important for understanding the behaviors of customers. In this paper, we study change mining in the contexts of decision tree classification for real-life applications.

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Introduction

The world around us changes constantly. Knowing and adapting to changes is an important aspect of our lives. For businesses, knowing what is changing and how it has changed is also crucial. There are two main objectives for mining changes in a business environment: 1. To follow the trends: The key characteristic of this type of applications is the word "follow". Companies want to know where the trend is going and do not want to be left behind. They need to analyze customers ' changing behaviors in order to provide products and

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