IJDKP - MOST CITED - Top 10 Papers

  • 1. Data Mining and Its Applications for Knowledge Management : A Literature Review from 2007 to 2012

    Tipawan Silwattananusarn and KulthidaTuamsuk
    September 2012 | Cited by 79

    Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.
  • 2. Analysis of Heart Diseases Dataset Using Neural Network Approach

    K.Usha Rani
    November 2011 | Cited by 77

    One of the important techniques of Data mining is Classification. Many real world problems in various fields such as business, science, industry and medicine can be solved by using classification approach. Neural Networks have emerged as an important tool for classification. The advantages of Neural Networks helps for efficient classification of given data. In this study a Heart diseases dataset is analyzed using Neural Network approach. To increase the efficiency of the classification process parallel approach is also adopted in the training phase.
  • 3. Predicting Students' Performance Using ID3 and C4.5 Classification Algorithms

    Vipul Honrao Kalpesh Adhatrao, Aditya Gaykar, Amiraj Dhawan and Rohit Jha
    September 2013 | Cited by 74

    An educational institution needs to have an approximate prior knowledge of enrolled students to predict their performance in future academics. This helps them to identify promising students and also provides them an opportunity to pay attention to and improve those who would probably get lower grades. As a solution, we have developed a system which can predict the performance of students from their previous performances using concepts of data mining techniques under Classification. We have analyzed the data set containing information about students, such as gender, marks scored in the board examinations of classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data, we have predicted the general and individual performance of freshly admitted students in future examinations.
  • 4. Diagnosis of Diabetes Using Classification Mining Techniques

    Ronak Sumbaly Aiswarya Iyer and S. Jeyalatha
    January 2015 | Cited by 44

    Diabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. The disease has been named the fifth deadliest disease in the United States with no imminent cure in sight. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing Decision Tree and Naïve Bayes algorithms. The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.
  • 5. A New Clutering Approach for Anomaly Intrusion Detection

    Ravi Ranjan and G. Sahoo
    March 2014 | Cited by 37

    Recent advances in technology have made our work easier compare to earlier times. Computer network is growing day by day but while discussing about the security of computers and networks it has always been a major concerns for organizations varying from smaller to larger enterprises. It is true that organizations are aware of the possible threats and attacks so they always prepare for the safer side but due to some loopholes attackers are able to make attacks. Intrusion detection is one of the major fields of research and researchers are trying to find new algorithms for detecting intrusions. Clustering techniques of data mining is an interested area of research for detecting possible intrusions and attacks. This paper presents a new clustering approach for anomaly intrusion detection by using the approach of K-medoids method of clustering and its certain modifications. The proposed algorithm is able to achieve high detection rate and overcomes the disadvantages of K-means algorithm.
  • 6. Incremental Learning: Areas and Methods - A Survey

    Prachi Joshi and Parag Kulkarni
    September 2012 | Cited by 33

    While the areas of applications in data mining are growing substantially, it has become extremely necessary for incremental learning methods to move a step ahead. The tremendous growth of unlabeled data has made incremental learning take up a big leap. Starting from BI applications to image classifications, from analysis to predictions, every domain needs to learn and update. Incremental learning allows to explore new areas at the same time performs knowledge amassing. In this paper we discuss the areas and methods of incremental learning currently taking place and highlight its potentials in aspect of decision making. The paper essentially gives an overview of the current research that will provide a background for the students and research scholars about the topic.
  • 7. A Prototype Decision Support System for Optimizing the Effectiveness of Elearning in Educational Institutions

    I. Zaqout S. Abu-Naser, A. Al-Masri and Y. Abu Sultan
    November 2011 | Cited by 33

    In this paper, a prototype of a Decision Support System (DSS) is proposed for providing the knowledge for optimizing the newly adopted e-learning education strategy in educational institutions. If an educational institution adopted e-learning as a new strategy, it should undertake a preliminary evaluation to determine the percentage of success and areas of weakness of this strategy. If this evaluation is done manually, it would not be an easy task to do and would not provide knowledge about all pitfall symptoms. The proposed DSS is based on exploration (mining) of knowledge from large amounts of data yielded from the operating the institution to its business. This knowledge can be used to guide and optimize any new business strategy implemented by the institution. The proposed DSS involves Database engine, Data Mining engine and Artificial Intelligence engine. All these engines work together in order to extract the knowledge necessary to improve the effectiveness of any strategy, including e-learning.
  • 8. Effective Arabic Stemmer Based Hybrid Approach for Arabic Text Categorization

    Abdelmonaime Lachkar, Meryeme Hadni and Said Alaoui Ouatik
    July 2013 | Cited by 32

    Text pre-processing of Arabic Language is a challenge and crucial stage in Text Categorization (TC) particularly and Text Mining (TM) generally. Stemming algorithms can be employed in Arabic text preprocessing to reduces words to their stems/or root. Arabic stemming algorithms can be ranked, according to three category, as root-based approach (ex. Khoja); stem-based approach (ex. Larkey); and statistical approach (ex. N-Garm). However, no stemming of this language is perfect: The existing stemmers have a small efficiency. In this paper, in order to improve the accuracy of stemming and therefore the accuracy of our proposed TC system, an efficient hybrid method is proposed for stemming Arabic text. The effectiveness of the aforementioned four methods was evaluated and compared in term of the F-measure of the Naïve Bayesian classifier and the Support Vector Machine classifier used in our TC system. The proposed stemming algorithm was found to supersede the other stemming ones: The obtained results illustrate that using the proposed stemmer enhances greatly the performance of Arabic Text Categorization.
  • 9. A Survey on Privacy Preserving Association Rule Mining

    G.Sudha Sadasivam and K.Sathiyapriya
    March 2013 | Cited by 31

    Businesses share data, outsourcing for specific business problems. Large companies stake a large part of their business on analysis of private data. Consulting firms often handle sensitive third party data as part of client projects. Organizations face great risks while sharing their data. Most of this sharing takes place with little secrecy. It also increases the legal responsibility of the parties involved in the process. So, it is crucial to reliably protect their data due to legal and customer concerns. In this paper, a review of the state-of-the-art methods for privacy preservation is presented. It also analyzes the techniques for privacy preserving association rule mining and points out their merits and demerits. Finally the challenges and directions for future research are discussed.
  • 10. Data, Text and Web Mining for Business Intelligence : A Survey

    Abdul-Aziz Rashid Al-Azmi
    March 2013 | Cited by 25

    The Information and Communication Technologies revolution brought a digital world with huge amounts of data available. Enterprises use mining technologies to search vast amounts of data for vital insight and knowledge. Mining tools such as data mining, text mining, and web mining are used to find hidden knowledge in large databases or the Internet.