Data Mining - Concepts and Techniques.

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A Break in the Clouds: Towards a Cloud Definition

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

Purpose – To review the academic literature on customer relationship management (CRM), provide a comprehensive bibliography and propose a method of classifying that literature.Design/methodology/approach – A range of online databases were searched to provide a comprehensive listing of journal articles on CRM. Six hundred articles were identified and reviewed for their direct relevance to CRM. Two hundred and five articles were subsequently selected. Each of these articles was further reviewed and classified. The review and classification process was independently verified. All papers were allocated to the main and sub‐categories based on the major focus of each paper.Findings – Papers and research on CRM falls into five broad categories (CRM – General, Marketing, Sales, Service and Support, and IT and IS) and a further 34 sub‐categories. The most popular areas covered by the papers lay in the sub‐category of CRM management, planning and strategy; and CRM general, concept, and study followed by papers in s...

Customer relationship management research (1992‐2002): An academic literature review and classification

Purpose – To examine the state of logistics and supply chain management (SCM) research in the last five years from the standpoint of existing methodologies. The state of research is assessed by examining the research design, number of hypothesis testing, research methods, data analysis techniques, data sources, level of analysis and country of authors.Design/methodology/approach – The review of SCM and logistics research is based on 442 papers published from 1999 to 2003 in the following three academic journals Journal of Business Logistics, International Journal of Physical Distribution & Logistics Management, and Supply Chain Management: An International Journal.Findings – Major findings show that there is an increase in the direct observation methods like case studies. In general, the research is more interpretive in nature. Survey method is still holding the highest position. More advanced techniques are being used for data analysis in empirical studies and there has been an increase in hypothesis tes...

Review of supply chain management and logistics research

The massive growth of data that are transmitted through a variety of devices and communication protocols have raised serious security concerns, which have increased the importance of developing advanced intrusion detection systems (IDSs). Deep learning is an advanced branch of machine learning, composed of multiple layers of neurons that represent the learning process. Deep learning can cope with large-scale data and has shown success in different fields. Therefore, researchers have paid more attention to investigating deep learning for intrusion detection. This survey comprehensively reviews and compares the key previous deep learning-focused cybersecurity surveys. Through an extensive review, this survey provides a novel fine-grained taxonomy that categorizes the current state-of-the-art deep learning-based IDSs with respect to different facets, including input data, detection, deployment, and evaluation strategies. Each facet is further classified according to different criteria. This survey also compares and discusses the related experimental solutions proposed as deep learning-based IDSs. By analysing the experimental studies, this survey discusses the role of deep learning in intrusion detection, the impact of intrusion detection datasets, and the efficiency and effectiveness of the proposed approaches. The findings demonstrate that further effort is required to improve the current state-of-the art. Finally, open research challenges are identified, and future research directions for deep learning-based IDSs are recommended.

Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues

Cloud computing provides a shared pool of computing resources that can be rapidly and elastically provisioned and released based on users’ demand to serve a wide and constantly expanding range of information processing needs. Due to its tremendous advantages this technology is maturing rapidly and is being adopted in many applications including government, business, and education. In this paper, we study how cloud computing can benefit e-learning education in KSA. We discuss the cloud computing educational environment and explore how universities and institutions may take advantage of clouds not only in terms of cost but also in terms of efficiency, reliability, portability, flexibility, and security. We present several case studies for educational clouds introduced by popular cloud providers which reflect the increasing interest in this new trend. We also discuss future challenges to cloud education.

http://www.airccse.org/journal/acij/papers/0112acij07.pdf

A new trend for e-learning in ksa using educational clouds

To stay competitive, hotels need to develop a viable customer retention strategy. Since a key to the successful development of such a strategy rests with customer relationship management, hotels should identify the most profitable ways to build and maintain a loyal customer relationship. In an effort to help hotels understand their customers’ preferences and the ways to interact with the customers, we propose data mining techniques. Based on a case study of luxury hotels in South Korea, this paper demonstrates the usefulness and practicality of the proposed data mining techniques.

A data mining approach to developing the profiles of hotel customers

Chronic driver turnover can adversely influence a trucking firm's competitiveness through disrupted delivery services, equipment down time and excessive recruiting expenses. Thus, a key to the survival of the trucking firm rests with its ability to recruit and retain qualified drivers who are less likely to cause turnover. In an effort to develop the ways to recruit and retain those drivers, we propose data mining techniques. Based on an empirical study of trucking firms in the USA, this paper not only develops a viable driver recruitment and retention strategy, but it also demonstrates the usefulness of the proposed data mining techniques.

Developing the profiles of truck drivers for their successful recruitment and retention

Most social media commentary in the Arabic language space is made using unstructured non-grammatical slang Arabic language, presenting complex challenges for sentiment analysis and opinion extraction of online commentary and micro blogging data in this important domain. This paper provides a comprehensive analysis of the important research works in the field of Arabic sentiment analysis. An in-depth qualitative analysis of the various features of the research works is carried out and a summary of objective findings is presented. We used smoothness analysis to evaluate the percentage error in the performance scores reported in the studies from their linearly-projected values (smoothness) which is an estimate of the influence of the different approaches used by the authors on the performance scores obtained. To solve a bounding issue with the data as it was reported, we modified existing logarithmic smoothing technique and applied it to pre-process the performance scores before the analysis. Our results from the analysis have been reported and interpreted for the various performance parameters: accuracy, precision, recall and F-score.

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Ahmed Emam

Papers

Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues

A new trend for e-learning in ksa using educational clouds

A data mining approach to developing the profiles of hotel customers

Developing the profiles of truck drivers for their successful recruitment and retention

Arabic Sentiment Analysis: A Survey