Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Analytic hierarchy process : An overview of applications

Fundamental statistics in psychology and education.

Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000-2006 covering 24 journals and proposes a classification scheme to classify the articles. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified. Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification, Customer Attraction, Customer Retention and Customer Development) and seven data mining functions (Association, Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization). Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper. The review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.

http://cjou.im.tku.edu.tw/bi2009/DM-usage.pdf

Review: Application of data mining techniques in customer relationship management: A literature review and classification

Recently, context-awareness has been a hot topic in the ubiquitous computing field Numerous methods for capturing, representing and inferring context have been developed and relevant projects have been performed Existing research has tried to determine user’s contextual information physically by using stereo type cameras, RFID, smart devices, etc Especially, these are focusing on physical context such as location, temperature, light, etc However, cognitive elements of the context are important and needs more study Therefore, this paper confines its research domain to the web-based information system (IS) and proposes an algorithm for inferring cognitive context in the IS domain First, a context inference algorithm aims at recognition of a user intention on the IS To apply this algorithm, we use a text categorization technique to classify various text-based sources users are using such as web pages, pdf document, MS-word documents, etc Second, the service inference algorithm is based on the similarity measurement between the user preference and the deliver-enabled services and tries to recommend a user-adaptive service to the user The context and service inference algorithm that this paper proposes can help the IS user to work with a IS conveniently and enable an existing IS to deliver ubiquitous service In this fashion, the paper shows the direction an existing IS heads and ultimately, shows the typical services of a ubiquitous computing-based IS

/pdf/a-study-of-context-inference-algorithm-on-the-web-based-1m3pybd49f.pdf

A Study of Context Inference Algorithm on the Web-based Information Systems

Composing a Structured Model from Validated Submodels for Effective Model Management

In rapidly changing business environments, all business areas have stiff competition to a gain competitive edge. In the telecommunication service area, Churn is a serious problem. Data mining techniques are commonly used to predict churn probability. In this paper, we propose a hybrid approach, CBR with SAGA(Simulated Annealing and Genetic Algorithm) approach, for predicting the churn rate of a telecommunication service company. Case-Based Reasoning is rapidly emerging AI technology that can use past experiences to solve current problems. Genetic Algorithm can be described as a mechanism that mimics the genetic evolution of a species. The basic idea of simulated annealing originates from thermodynamics and metallurgy. This annealing process is mimicked by a local search strategy. In this hybrid approach, SAGA determines weight vector of CBR using SAGA learning. Churn rate prediction results are compared to other approaches - Logistic Regression and CBR with GA. An architecture of a decision support system which focuses on reducing the churn rate of telecom company, is also suggested.

/pdf/a-study-on-reducing-churn-rate-of-telecommunication-company-41n6eywsfd.pdf

A Study on Reducing Churn Rate of telecommunication company using Case-based Reasoning

Corrigendum: Corrigendum to A maturity-model based CoP evaluation framework: A case study of strategic CoPs in a Korean company [Expert Systems with Applications 37 (3) (2010) 2670-2681]

Since the early 1980s, the concept of relationship management in marketing area has gained its importance. 
Acquiring and retaining the most profitable customers are serious concerns of a company to perform more targeted marketing campaigns. For effective CRM (Customer Relationship Management), it is important to gather information on customer value. Many researches have been performed to calculate customer value based 
on CLV (Customer Lifetime Value). It, however, has some limitations. It is difficult to consider the churn of customers, because the previous prediction models have focused mainly on expected future cash flow derived from customers' past profit contribution. 
In this paper we suggest a CL V model considering past profit contribution, potential benefit, and churn probability of a customer. We also cover a framework for analyzing customer value and segmenting customers based on their value. Customer value is classified into three categories: current value, potential value and customer loyalty. Customers are segmented according to the three categories of customer value. A case study on calculating customer value of a wireless communication company will be illustrated.

Euiho Suh

Papers

A Study of Context Inference Algorithm on the Web-based Information Systems

Composing a Structured Model from Validated Submodels for Effective Model Management

A Study on Reducing Churn Rate of telecommunication company using Case-based Reasoning

Corrigendum: Corrigendum to A maturity-model based CoP evaluation framework: A case study of strategic CoPs in a Korean company [Expert Systems with Applications 37 (3) (2010) 2670-2681]

[Session C1 데이터마이닝/CRM]A CLV (Customer Lifetime Value) model in the wireless telecommunication industry