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It is an integrated and interconnected process. IJDMMM publishes research papers, innovative ideas, reviews, surveys, debates, reports, case studies, position notes, practice comments, book reviews, commentaries, and news. Special issues devoted to important topics in data mining, modelling and management will occasionally be published. Editor in Chief Prof. About this journal Editorial board Submitting articles. Objectives IJDMMM aims to provide a professional forum for formulating, discussing and disseminating these solutions, which relate to the design, development, deployment, management, measurement, and adjustment of data warehousing, data mining, data modelling, data management, and other data analysis techniques.

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Journal of Biomedical Informatics, 40, Procedia Computer Science, 1 1 , A framework for early warning and proactive control systems in food supply chain networks. Computers in Industry, 61, — Mining customer knowledge for product line and brand extension in retailing. Expert Systems with Applications, 34 3 , Knowledge management technologies and applications-literature review from to Expert Systems with Applications, 25, Mining group-based knowledge flows for sharing task knowledge.

Decision Support Systems,50 2 , Revealing research themes and trends in knowledge management: From to Knowledge-Based Systems. Knowledge Management and the Dynamic Nature of Knowledge. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36, Knowledge Management Tools. Boston: Butterworth-Heinemann.

Information technology as a facilitator for enhancing dynamic capabilities through knowledge management. The effects of information technology on knowledge management systems. Expert Systems with Applications, 35, Textual data mining for industrial knowledge management and text classification: A business oriented approach. Expert Systems with Applications, 39, Investigation on Technology Systems for Knowledge Management.

A knowledge management approach to data mining process for business intelligence. Data mining for exploring hidden patterns between KM and its performance. Knowledge-Based Systems, 23, Usha Rani Dept. 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. Anil Jain, Jianchang Mao and K. Widrow, D. Rumelhard, and M. ACM, vol. Kusiak, K. Kernstine, J. Kern, K A. McLaughlin and T. Burke, P. Goodman, D.

Rosen, D. Henson, J. Weinstein, F. Harrell, J. Marks, D. Winchester, and D. Kamruzzaman , Md. Dybowski and V. Er, N. Yumusak and F. Temurtas, "Chest disease diagnosis using artificial neural networks", Expert Systems with Applications, Vol. Moein, S. Monadjemi and P. According to the WHO report, by this number is expected to rise to over 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 ontinued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented.

The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients. Kumari and A. Velu and K. S and Dr Pramananda Perumal. Jayalakshmi and Dr. Michael Kipp, Dr. Alassane Ndiaye and Dr. 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. 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. Staniford-Chen, S. Cheung, R. Dilger, J. Frank, J. Hoagland, K. Levitt, C. Wee, R. Yip, D. Jianliang, S. Haikun and B. International Forum on Information Technology and Application, Ghorbani and Nabil Belacel. Y-means: a clustering method for Intrusion Detection.

Muda, W. Yassin, M. Sulaiman and N. Fatma, L. Chandrasekhar, K. Abu-Naser, A. Al-Masri, Y. Abu Sultan , I. 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. Kamberm Data mining: concepts and techniques. San Francisco: Jossey-Bass. Generalization and decision tree induction: efficient classification in data mining. Educational Data Mining: a Case Study. LOOI, G. Discovering enrollment knowledge in university databases. In KDD, pp. Data mining, knowledge management in higher education, potential applications. In workshop associate of institutional research international conference, Toronto, pp.

An academic decision-support system based on academic performance evaluation for student and program assessment, International Journal of Engineering Education, Vol. Using genetic algorithms for data mining optimizing in an educational web-based system. Adaptive decision support for academic course scheduling using intelligent software agents. Modeling multidimensional databases. IBM Research Report. Piatetsky-Shapiro and W. Frawley, editors, Knowledge Discovery in Databases, pp.

Management information Systems. Prentice Hall; 11th edition. Vol 1 ,No 2. 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. Lui, J. Cai, J. Yin, A. Fahim, G.

Saake, A. Salem, F. Torky, M. Ramadan, K-means for spherical clusters with large variance in sizes, Journal of World Academy of Science, Engineering and Technology, Camastra, A. Shen, H. Yu, Y. Kamiya, O. Zhang, R. Ramakrishnan, M. Livny, Birch: An efficient data clustering method for very large databases, Proc.

Conference on Management of Data, , pp. Deelers, S. Auwantanamongkol, Enhancing k-means algorithm with initial cluster centers derived from data partitioning along the data axis with highest variance, International Journal of Electrical and Computer Science, , pp Young, A. Arel, T. Karnowski, D. Charikar, C. Chekuri, T. Feder, R. Motwani, Incremental clustering and dynamic information retrival, Proc. Hammouda, Incremental document clustering using Cluster similarity histograms, Proc.

Su, Y. Wan, Y. Qin, A fast incremental clustering algorithm, Proc. Data Mining , Followers. Papers People. Save to Library. Thus, unraveling the relationships between ncRNAs and other biomedical entities in Thus, unraveling the relationships between ncRNAs and other biomedical entities in cells are critical for better understanding ncRNA roles that may eventually help develop their use in medicine.

DES-ncRNA is a knowledgebase containing text- and data-mined information from public scientific literature and other public resources. Exploration of mined information is enabled through terms and pairs of terms from 19 topic-specific dictionaries including, for example, antibiotics, toxins, drugs, enzymes, mutations, pathways, human genes and proteins, drug indications and side effects, mutations, diseases, etc.

With investigation on recent blackouts around the world, the three-defence-lines criterion, which has been the security standard for power systems in China, is reviewed. An adaptive space-time cooperative framework, named as Wide ARea It upgrades the individual defence lines to a global scheme. Its essentials are: 1 acquiring wide area measures by using RTUs, PMUs, fault recorders, protection management systems and system protection schemes; 2 integrating all information acquired via various equipments into a unified platform; 3 performing trajectory-based data mining and quantitative security analysis for time response curves obtained by PMUs or simulations; 4 adaptive optimization of preventive control, emergency control, corrective control and recovery control; 5 coordination among these controls; 6 coordinatin among WARMAPs in a multi-layer power system.

Reasoning with Noisy Software Effort Data. Recently, machine learning classifiers have been used successfully for software effort evaluation decisions. However, the development However, the development and validation of these classifiers and other modes require good quality data. Most research on machine learning assumes that the attributes of training and tests instances are not only completely specified but are also free from noise.

Real-world industrial datasets, however, suffer from corruption or noise that is not always known. However, blindly applying such machine learning techniques to noisy software effort evaluation data may fail to make very good or perfect predictions, resulting in poor decisions and ineffective project management.

This article investigates the effect of noisy domains on the learning accuracy of eight machine learning and statistical pattern recognition algorithms. We further derive solutions for the problem of noisy domains in software effort prediction from a probabilistic point of view. Our experimental results show that our algorithm can improve prediction for software effort corrupted by noise with reasonable and much improved accuracy.

Conformance checking of electronic business processes to secure distributed transactions. Providing context-based security for inter-organizational electronic business processes. The analysis is based on process mining techniques and uses observations taken at all relevant The analysis is based on process mining techniques and uses observations taken at all relevant communication layers e. The context based analysis presented here allows the simple implementation of complex security and compliance policies.

The new window density function for efficient evolutionary unsupervised clustering. Generalizing the -Windows clustering algorithm in metric spaces. Unsupervised distributed clustering. Data Clustering Using Wave Atom. Apollo: Towards factfinding in participatory sensing.

This demonstration presents Apollo, a new sensor information processing tool for uncovering likely facts in noisy participatory sensing data. Participatory sensing, where users proactively document and share their observations, has Participatory sensing, where users proactively document and share their observations, has received significant attention in recent years as a paradigm for crowd-sourcing observation tasks.

However, it poses interesting challenges in assessing confidence in the information received.

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Jo, and N. Japkowicz, in Computational Intelligence , 20 1 , Chawla, K. Boyer, L. Hall, and W. Kegelmeyer, Journal of Articifial Intelligence Research , Liu, J. Ghosh, and C. Forman and I. Visa and A. Chawla, L. Hall, and A. Drummond and R. Juszcak and R. Atlas, and R. Ladner, Machine Learning 15 2 , , May Zheng and B. Padmanabhan, In Proc. Saar-Tsechansky and F. Provost, Machine Learning , Meek, B.

Thiesson, and D. Heckerman, Journal of Machine Learning Research , Active Sampling for Feature Selection , S. Veeramachaneni and P. Lewis and J. Weiss and F. Provost, Journal of Artificial Intelligence Research, , Chan and S. Stolfo, KDD Which led him to launch this tech portal in He is now a regular contributor to RoboticsBiz.

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