Lessons learned: A case study using data mining in the newspaper industry

Objectives: Clustering algorithm is broadly used as spectral algorithm in social media, where a reference of contact is used and mined further for various combinations of suggested friends and lookups. This paper Identifies key lead generation techniques to be used in customer relationship management for sales marketing to decide data gathering. Also to define key merits and demerits of these techniques and to prepare a Matrix of comparison of these techniques to justify the data source and data set. M-Cluster algorithm is used for lead qualification i.e. training set preparation and data evaluation. Methods: Todefine a training set based on various attributes/fields in the data given for classification. This training set is used to run the data process and to produce expected result. This is assessed and accepted for definite data set processing or additional run for interim training data set preparation. Findings: This study is taken to customize clustering algorithm for data mining process in a customer relationship management field as the space of data is more and variant. Also proving the usability of customized clustering algorithm in data mining and the efficiency in processing mechanism compared to other methods used in current situation of data mining in customer relationship management is the part of this study. The customized algorithm developed as part of this study considers the two major areas of data mining using clusters. Applications: The results produced tremendous trends that the clustered algorithm suits to any data mining process when scaling and data classification are diversified and less in control.Keywords: Classification, Clustering Algorithm, Data Mining, K-Means, Lead Generation, M-Clustering Algorithm

Recent advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from huge database. Data mining (DM) technology has emerged as a means of performing this discovery. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Many data mining methods and algorithms have been developed and applied in a lot of application fields [1]. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining [2]. In this talk, some basic issues of data mining are addressed. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we should obey in a data mining process? Through analyzing existing data mining methods, and domain-driven (or user-driven) data mining models [3-5], we find that we should take a data mining process as a process of knowledge transformation. Based on this understanding of data mining, a conceptual data mining model of domain-oriented data-driven data mining (3DM) is proposed [2]. The relationship between traditional domain-driven (or user-driven) data mining models and our proposed 3DM model is also analyzed. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [6], decision tree [7], and concept lattice [8] from database are proposed. The experiment results for these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by our 3DM data mining algorithms.

Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial problems such as customer relationship management (CRM), are useful in pointing out customers who are likely attritors and customers who are loyal, but they require human experts to postprocess the discovered knowledge manually. Most of the postprocessing techniques have been limited to producing visualization results and interestingness ranking, but they do not directly suggest actions that would lead to an increase in the objective function such as profit. In this paper, we present novel algorithms that suggest actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal) while maximizing an objective function: the expected net profit. These algorithms can discover cost-effective actions to transform customers from undesirable classes to desirable ones. The approach we take integrates data mining and decision making tightly by formulating the decision making problems directly on top of the data mining results in a postprocessing step. To improve the effectiveness of the approach, we also present an ensemble of decision trees which is shown to be more robust when the training data changes. Empirical tests are conducted on both a realistic insurance application domain and UCI benchmark data

Pavement deflection data are often used to evaluate a pavement’s structural condition nondestructively. Pavement layers are important parameters in view of bearing capacity. Pavement layer thickness may be known from the design project or site investigation. At the same time, using backcalculation analysis, flexible pavement layer thicknesses together with in situ material properties can also be backcalculated from the measured field data through appropriate analysis techniques. Data mining (DM) process has not been used as a backcalculation tool before. In this study, DM process is used in backcalculating the pavement layer thickness from deflections measured on the surface of the flexible pavements. Experimental deflection data groups from NDT are used to show the capability of the DM process in backcalculating the pavement layer thickness and compared each other. Performing the study, modeling with Kstar method gives fine results with respect to other DM modeling techniques. Backcalculation of pavement layer thickness using DM process has been carried out for the first time.

Data mining (DM) is a powerful information technology (IT) tool in today’s competitive business world, especially as our human society entered the Big Data era. From academic point of view, it is an area of the intersection of human intervention, machine learning, mathematical modeling and databases. In recent years, data mining applications have become an important business strategy for most companies that want to attract new customers and retain existing ones. Using mathematical techniques, such as, neural networks, decision trees, mathematical programming, fuzzy logic and statistics, data mining software can help the company discover previously unknown, valid, and actionable information from various and large sources (either databases or open data sources like internet) for crucial business decisions. The algorithms of the mathematical models are implemented through some sort of computer languages, such as C++, JAVA, structured query language (SQL), on-line analysis processing (OLAP) and R. The process of data mining can be categorized as selecting, transforming, mining, and interpreting data. The ultimate goal of doing data mining is to find knowledge from data to support user’s decision. Therefore, data mining is strongly related with knowledge and knowledge management.

Analysing datasets using data mining techniques can enhance decision making in organizations. However, to ensure that the full potential of these techniques is realised it is important that decision makers understand there are Knowledge Discovery and Data Mining (KDDM) processes that are mature enough to be adopted. This paper demonstrates the benefits of using a KDDM process to evaluate survey data for internet banking users in Jamaica which includes demographic as well as attitudinal and behavioral variables. The major benefits of following this process include the selection of a set of models, rather than a single model, which are more relevant to the business/research objectives and use of a more targeted knowledge discovery process as the data mining analyst is now directed to consider the effects the decisions in each phase will have on subsequent phases. This leads to more relevant knowledge being extracted from the data mining process.

Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge discovery large-scale database. Data mining technology is a useful tool for this task. It is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately,most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we would obey in a data mining process? What is the relationship between the prior knowledge of domain experts and the knowledgemind from data? In this paper, we will address these basic issues of data mining from the viewpoint of informatics [1]. Data is taken as a manmade format for encoding knowledge about the natural world. We take data mining as a process of knowledge transformation. A domain-oriented data-driven data mining (3DM) model based on a conceptual data mining model is proposed. Some data-driven data mining algorithms are also proposed to show the validity of this model, e.g., the data-driven default rule generation algorithm, data-driven decision tree pre-pruning algorithm and data-driven knowledge acquisition from concept lattice.

Preface. Acknowledgements. 1: Why Human-Centered e-Business? 1.1. Introduction. 1.2. e-Business and e-Commerce. 1.3. Converging Trends towards Human-Centeredness. 1.4. Technology-Centeredness vs Human-Centeredness. 1.5. Human-Centered Approach. 1.6. Organization Levels and e-Business. 1.7. Summary. References. 2: e-Business Concepts and Technologies. 2.1. Introduction. 2.2. e-Business Systems. 2.3. e-Business Strategies. 2.4. e-Business Models. 2.5. Internet and Web Technologies. 2.6. Intelligent Technologies. 2.7. Software Engineering Technologies. 2.8. Multimedia. 2.9. Summary. References. 3: Converging Trends towards Human-Centeredness and Enabling Theories. 3.1. Introduction. 3.2. Pragmatic Considerations for Human-Centered System Development. 3.3. Enabling Theories for Human-Centered Systems. 3.4. Discussion. 3.5. Summary. References. 4: Human-Centered e-Business System Development Framework. 4.1. Introduction. 4.2. Overview. 4.3. External and Internal Planes of Human-Centered Framework. 4.4. Components of the Human-Centered e-Business System Development Framework. 4.5. Activity-Centered e-Business Analysis Component. 4.6. Problem Solving Ontology Component. 4.7. Summary. References. 5: Human-Centered e-Business System Development Framework. 5.1. Introduction. 5.2. Problem Solving Ontology Component. 5.3. Human-Centered Criteria and Problem Solving Ontology.5.4. Transformation Agent Component. 5.5. Multimedia Interpretation Component. 5.6. Application of Multimedia Interpretation Component in Medical Diagnosis. 5.7. Emergent Characteristics of HCVM. 5.8. Summary. References. 6: e-Sales Recruitment. 6.1. Introduction. 6.2. Human Resources Management e-Business Systems. 6.3. Information Technology and Recruitment. 6.4. Activity-Centered e-Business Analysis of Sales Recruitment Activity. 6.5. Human-Centered Activity Model. 6.6.Implementation and Results. 6.7. Summary. References. 7: Customer Relationship Management and e-Banking. 7.1. Introduction. 7.2. Traditional Data Mining and Knowledge Discovery Process. 7.4. Data Mining and the Internet. 7.5. Multi-layered, Component-based Multi-Agent Distributed Data Mining Architecture. 7.6. Application in e-Banking. 7.7. Data Mining Implementation Results. 7.8. Summary. References. 8: HCVM Based Context-Dependent Data Organization for e-Commerce. 8.1. Introduction. 8.2. Context-Dependent Data Management. 8.3. Context-Modeling in XML. 8.4. Flexible Access to Context Information. 8.5. Sample Interaction. 8.6. Summary. References. 9: Human-Centered Knowledge Management. 9.1. Introduction. 9.2. HCVM Approach to Knowledge Sharing and Decision Support in Knowledge Management Systems. 9.3. Resource Description Format (RDF) for Knowledge Representation. 9.4. The Regional Innovation Leadership (RIL) Cycle. 9.5.

The data mining experiments from this chapter are a component of a larger research which is to be used to make several correlations, analysis in order to integrate data mining process in the managerial system for optimal decision support. I offered an insight of how data mining processes are being applied in the large spectrum of education by presenting recent applications and studies published in the scientific literature, considered to be relevant to the development of this emerging science. I presented my work through a number of experiments conducted over questionnaires data and scholastic databases at Faculty of Economics and Business Administration Cluj-Napoca, using classification learning and data clustering methods. In the last part of the chapter I introduced the concept of Academic Intelligence Management, and illustrated the integration of data mining processes and their particular role in higher education management and decision support. The studies will continue with deeper mining of educational topics, such as performance in scientific research, correlations between the students' knowledge and the competences demanded on the labor market, academic failure, to perceive what and how much the students know, to realize learning gaps, and also improve teaching methods and educational management processes.

Data is identified as the fuel of modern society for its versatility of use and effectiveness of use. In addition, modern businesses are making a decline based on analysis of historical data and patterns of the data. Such dependency on data analysis makes the process of data analysis important for data mining. Therefore the overall study has shed light on the significance of the data mining process and extraction process of data in order to make a data-driven decision. Additionally, the problems related to the process of data extraction and data mining are mentioned in the study which helps to achieve an overall concept for the data extraction and data mining process. Additionally, the significance of the process is mentioned in the study. Additionally, there are tables constructed that represent problems of the data extraction process and mining and the significance of the stems of mining. The study concludes in a way that helps in the implication of data extraction methods for business.

In the ever-increasing competitive and customer-centered global market, the role of Customer Relationship Management (CRM) is essential to the performance, longevity, competitiveness and profitability of enterprises. The study aims to provide an overview of the impact of emerging technologies on traditional CRM and Social CRM. Therefore, through a critical review, this study explores the use of Artificial Intelligence (AI), Blockchain, Big Data Analytics (BDA), Data Mining and Machine Learning (ML) technologies in CRM. Based on the results, the above-mentioned technologies can significantly improve CRM. More specifically, their successful implementation enables enterprises to stay ahead of competition, to get to know their customers, improve their overall performance, Quality of Experience (QoE) and Quality of Service (QoS), increase profits, and reduce time consumption, risks and costs. Finally, by integrating these technologies, enterprises will offer more personalized and customized products and services and greatly improve customers' satisfaction, acquisition, relationships, loyalty, experience and retention.

Data mining techniques for customer relationship management

<span lang="EN-US">Data mining is a computational approach aiming to discover hidden and valuable information in large datasets. It has gained importance recently in the wide area of computational among which many in the domain of Business Informatics. This paper focuses on applications of data mining in Customer Relationship Management (CRM). The core of our application is a classifier based on the naive Bayesian classification. The accuracy rate of the model is determined by doing cross validation. The results demonstrated the applicability and effectiveness of the proposed model. Naive Bayesian classifier reported high accuracy. So the classification rules can be used to support decision making in CRM field. The aim of this study is to apply the data mining model to the banking sector as example case study. This work also contains an example data set related with customers to predict if the client will subscribe a term deposit. The results of the implementation are available on a mobile platform. </span>

This research study analyzes the different data mining techniques used for the detection of credit card frauds. Additionally, different data mining techniques are properly described in this study. The steps of data mining are clearly explained in this study, which are helpful for recognizing the fraudulent activities. This study is discussing that Bayesian network, and decision tree are the effective techniques of data mining. The issues of data mining are also discussed in this study, which is creating performance issues, and user interaction issues in the financial institutions. Therefore, this study is providing the importance of RF algorithm in the determination of the credit card fraud. Data cleaning, and data visualization, therefore, the machine learning process is allowed to be developed with the support of the data mining process of credit cards. Clustering is the main process that is takes place in this data mining process. Additionally, the sequential pattern is being highlighted by this process, which helps to improve the fraud detection process of credit cards.

Customer Relationship Management (CRM) has attracted much attention due to the high competition in the retail trade. Meanwhile, data mining has great potential to improve the effectiveness of CRM, since the data volume of customers is drastically increasing. In this paper, we firstly propose a logic structure of the retail CRM system, and thus discuss several data mining techniques, i.e., classification, clustering, association analysis, for CRM. Last but not the least, several sub-fields of CRM that are suitable to the application of data mining techniques are discussed. As can be seen from this paper, the retailers must strengthen the application of data mining technology research in order to support the management decision-making and improve the level of information management.KeywordsRetailCustomer relationship managementData mining