315
An Intelligent Transformation Knowledge Mining Method Based on Extenics
An Intelligent Transformation Knowledge Mining Method Based on Extenics Xingsen Li1, Haolan Zhang1, Zhengxiang Zhu3, Zhongbiao Xiang4, Zhengxin Chen2, Yong Shi2,5 1 School of Management, Ningbo Institute of Technology, Zhejiang University, China 2 College of Information Science & Technology, University of Nebraska at Omaha, USA 3 Graduate School, National Defense University PLA China, China 4 School of Management, Zhejiang University, China 5 Research Center on Fictitious Economy and Data Science, CAS, China {lixs, haolan.zhang}@nit.zju.edu.cn,
[email protected],
[email protected], {zchenu, yshi}@unomaha.edu
Abstract With the rapid development of internet technology, more and more businesses are running on the Web and lots of business data have been stored in databases or log files in web servers. How to make better decisions on the huge amount of data is an urgent task. Knowledge acquisition through data mining becomes one of the most important directions to support scientific decision making; however, the knowledge discovered from data mining may not work effectively because most of it only describes static knowledge, not how-to-do knowledge. In this paper, we describe our approach of dealing with this problem, starting with Multiple Criteria Linear Programming (MCLP) based classification which classifies the web data sets and find the best match elements, but the models are unexplainable, decision tree mined the explainable rules, but it is only static know-what knowledge, we still don’t know how to transfer class bad to class good. In order to improve such kind of situation, we focus on a new methodology for discovering actionable know-how knowledge based on decision tree rules and Extension set theory. Our second level mining method can extract a set of hidden patterns describing how to change churn customers’ behaviors and provides "from can’t to can, from bad to good" rules. It successful provides decision making support on the transformation of the customer churn. A case study of a web company has shown that our method is feasible and effective. It also has the reference value for data mining in the web businesses. Keywords: Extenics, Transformation, Decision tree, Knowledge mining, MCLP.
1 Introduction The World Wide Web has become a new business platform and accumulated huge data. Billions of pages are publicly available, and it is still growing dramatically. Almost all the entrepreneurs desire to make wise decisions to support strategy and make more profit. However, it appeared to be inadequate that the decision-making is made
by several managers beyond the mass of information come from complex business. Web Mining and text mining can discover a lot of knowledge from amounts of web pages and local text files etc [1]. Data mining provides a new kind of knowledge such as rules from data for better decision making [2]. There are many data mining methods available. Multiple criteria linear programming (MCLP) has been used in classification for more than twenty years [3] and has been successfully applied in many real applications for business intelligence [4-6]. It has a higher accuracy on prediction for classifying. However, one of the possible limitations of MCLP is that it generates unexplainable models which can only tell us classification results without reasons [7]. Decision tree based classification [8] with stronger interpretability of classification principle is one of the most commonly used data mining methods [9], especially in business intelligence [10-11]. Rule mining would offer profound insight into discovering, distinguishing properties of objects. However, decision tree only resulted in static descriptions on characteristics of certain class type [12]. It mostly provides know-what rules, although it produces numerous rules, the proportion of rules truly interested is too less. How to re-mine such rules in order to get actionable knowledge for a better decision making is quite a challenging problem. In general, the knowledge from data mining can be rules, scoring formulas or models et al. But not all the knowledge can be used well in applications, as this kind of new knowledge from data mining is huge in quantity, ragged in quality and will be updated quickly [13]. Such kind of knowledge goes far beyond the traditional knowledge management based on human being’s experience. In other words, much pattern knowledge obtained from data mining is not practical enough; it is rough knowledge with impurity in it [13] and requires a further analyzed combination with expert’s experience for business management. Extenics (formerly called Extension Theory) is a new discipline engaged in studying the extension properties of things and transformation methods for solving contradictory problems with formula models [14]. It is an
*Corresponding author: Xingsen Li; E-mail:
[email protected] DOI: 10.6138/JIT.2013.14.2.15
15-Li.indd 315
2013/3/22 上午 10:35:44
316
Journal of Internet Technology Volume 14 (2013) No.2
inter-discipline of information, mathematics, philosophy and engineering. Extenics potentially provides a way to improve utility of rules beyond decision tree data mining. In particular, Extenics will help us effectively acquire know-how knowledge, such as transformation rules which can transform the users from an unfavorable or undesirable position to a favorable or desirable position (such as from bad to good). The main purpose of knowledge management and data mining is to aid people make wiser decisions and finally solve contradictory problems. In this paper, we try to explore a new solution to obtain actionable know-how knowledge based on Extenics and improve the practical value of MCLP classification and decision tree rules. The rest of the paper is organized as follows: In Section 2 we review the related work especially on MCLP and decision tree method, in Section 3 we analyze MCLP, decision tree and extension set theory (one theory of Extenics ) for preparing our new solutions. In Section 4 we present the algorithm design and implementation of transformation knowledge in detail. Section 5 will give a case study and shows how this new methodology works. In Section 6 we conclude the paper and give future research directions.
2 Related Work We need knowledge to explain many business phenomenons such as why customers churn. There exists a variety of definitions and classifications of knowledge. Alavi viewed knowledge from five perspectives [15]: (1) A state of mind. (2) An object to be stored and manipulated. (3) A process of applying expertise. (4) Access to information. (5) A capability. Researched on many academics’ study, Biggam [16] gave a “Knowledge Types” matrix which Divided knowledge into four categories: Tacit vs. Explicit, Personal vs. Organizational, Dynamic vs. Static, Internal vs. External. Many classification data mining methods discover static knowledge which only describe what without why let alone how. Classification results from MCLP are difficult to tell us the hidden reasons. To overcome this shortage, a knowledge mining strategy which mines from blackbox MCLP models to get explainable and understandable knowledge is proposed [17]. Then Zhang et al. went further presented a knowledge mining strategy which mines explainable decision rules from MCLP models [7] based on rough set theory, and get explainable knowledge in two ways: a clustering based decision rule extraction approach to extract knowledge from the definable set, and a rough set
15-Li.indd 316
based rule extraction approach to the rough set. According to the concept of Pareto optimality, MCLP models for multiple classes and unbalanced training set are constructed separately, then regularized MCLP (RMCLP) model is constructed to minimize the sum of the deviations (MSD) among the observations and to maximize the minimum distances (MMD) of observations from the critical value [18]. Decision tree can show us classification rules. But when dealing with the complex data, it is not enough only relied on classification rules aiming to prevent the loss of customers and we need to get transformation knowledge [19]. To get better decisions, Boutsinas and Athanasiadis presented an efficient algorithm for combining partial classification rules of different classifiers supplied with different subsets of the data [20]. Extenics has been applied to many fields such as information, automation, designing, controlling and management, which have obtained good achievements. [21]. Li et al. [22] combined the Extenics with artificial intelligence, database technology, and software engineering to build a software system called Extension Strategy Generating System (ESGS) which enable computers to aid strategy generation. The idea of ESGS is an inevitable trend towards scientific and intelligent decision making. Combining Extenics with data mining, a new methodology called Extension data mining (EDM) [23] has been build up. The main task of EDM is to acquire the transformation knowledge to help for better decisions. It takes advantage of extension methods and data mining technology, explores to acquire extensible classification knowledge by extension transformations. Also the conception of Extension Knowledge [24] has been proposed and some methods were discussed by utilizing properties of matter element and transformations based on Extenics [13][19]. As knowledge or hidden patterns discovered by data mining from large databases has great novelty, to bridge the gap between data mining and knowledge management Zhang et al. [25] established foundations of intelligent knowledge management base on the hidden patterns created by data mining. It enables to systematically analyze the process of intelligent knowledge management from original data, rough knowledge, intelligent knowledge, and actionable knowledge as well as four transformations methods. Such research made a good start, but neither has put forward a practical theory to mine know-how knowledge based on the improvement of decision tree classifications nor transformation knowledge which can transform object from an unfavorable or undesirable position to a favorable or desirable position.
2013/3/22 上午 10:35:44
An Intelligent Transformation Knowledge Mining Method Based on Extenics
3 Preparing for Transformation Knowledge Mining 3.1 MCLP Based Classification Considering a two-group classification problem, assume we have a training set A = {A1, A2, ..., An}which has n instances, each instance has r attributes. We define a boundary vector b to distinguish the first group G1 and the second group G2. Then we can establish the following linear non-equality functions [3]
Then a general problem of data classification by using multiple criteria linear programming (MCLP) can be described as [3-4]: Given a set of classes and a set of attribute variables, one can use a linear programming model to define a related boundary value (or variables) to separate the classes. Each class is then represented by a group of constraints with respect to a boundary in the linear program. A model for two predefined classes is given in Figure 1.
Figure 1 A Two-Classes MCLP Model
Given two attributes {a1, a2} and two groups {G1 , G2}, with the observation Ai = (Ai1, Ai2), we want to find a scalar b and nonzero vector X = (x1, x2) such that AiX ≤ b, Ai ∈ G1 and AiX ≥ b, Ai ∈ G2 have the fewest number of violated constraints. Let ai = the overlapping of two-group (classes) boundary for case Ai (external measurement); a = the max overlapping of two-group (classes) boundary for all cases Ai (ai < a); bi = the distance of case Ai from its adjusted boundary (internal measurement); b = the min distance of all cases Ai to the adjusted boundary (bi > b); Multi-Criteria Linear programming considers the total distance from the boundary of two groups [6]: Minimize Siai and Maximize Sibi
15-Li.indd 317
317
Subject to AiX = b + ai – bi, Ai ∈ G1, Ai X = b – ai + bi, Ai ∈ G2, where Ai are given, X and b are unrestricted, and ai and bi ≥ 0. Based on above formulas, we select the middle area between Lm and Ln as main elements for transformation from bad to good. 3.2 Obtain Primary Rules by Decision Tree Decision tree is a tree structure similar to flow chart, where each internal node represents a test on attribute, each branch represents output of test, and each leaf node represents a category [26]. Decision tree is mainly based on the summarized data attribute values, from the tree top level node (root node) to leaf node traversal which store the forecast sample, through this procedure it can transform the decision tree into an “if-then” form of classification rules [8] [27]. Rule 2: (198/14, lift 2.7) whether using mobile-mail services = 0 POINTS 92 Type = 6 class B [0.925] Rule 3: (6, lift 2.7) whether using mobile-mail services = 1 POINTS
