Computational Intelligence in Data Mining - Volume 3: by Lakhmi C. Jain, Himansu Sekhar Behera, Jyotsna Kumar Mandal,

By Lakhmi C. Jain, Himansu Sekhar Behera, Jyotsna Kumar Mandal, Durga Prasad Mohapatra

The contributed quantity goals to explicate and handle the problems and demanding situations for the seamless integration of 2 center disciplines of desktop technological know-how, i.e., computational intelligence and information mining. information Mining goals on the automated discovery of underlying non-trivial wisdom from datasets via using clever research recommendations. The curiosity during this study sector has skilled a substantial development within the final years because of key elements: (a) wisdom hidden in corporations’ databases may be exploited to enhance strategic and managerial decision-making; (b) the massive quantity of information controlled through businesses makes it most unlikely to hold out a handbook research. The e-book addresses diverse equipment and methods of integration for boosting the final objective of information mining. The ebook is helping to disseminate the data approximately a few leading edge, energetic study instructions within the box of information mining, computing device and computational intelligence, in addition to a few present matters and functions of similar issues.

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Extra info for Computational Intelligence in Data Mining - Volume 3: Proceedings of the International Conference on CIDM, 20-21 December 2014 (Smart Innovation, Systems and Technologies)

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3 Formulation of the Project Selection Problem In project selection, a decision-maker is deals with the problem of selecting an appropriate subset of projects from an inappropriate set of projects based on a set of selection criteria. This process is known as the multi-criteria decision making (MCDM) process [15–17]. In general, there are two types of MCDM problems: multi-attribute decision making (MADM) problem and multi-objective decision making (MODM) problem. Based on this categorization, multi-criteria project selection problem is seen as a distinctive MADM problem in terms of the characteristics of project selection.

Applying UML and Patterns—An Introduction to Object-Oriented Analysis and Design and Iterative Development 3rd edn. Pearson Education, Ghaziabad (2005) 4. : Capturing non-functional software requirements using the user requirements notation. In: Proceedings of The International Research Conference on Innovations in Information Technology, India (2004) 5. : Functional grouping of natural language requirements for assistance in architectural software design. J. Knowl. Based Syst. 30(1), 78–86 (2012) (Elsevier) 6.

Analogy based estimation is a Non-algorithmic method in that a critical role is played by the similarity measures between a pair of projects. Here, a distance is calculated between the software project being estimated and each of the historical software projects. It then finds the most similar project that is used to estimate the cost. Estimation by analogy is essentially a form of Case Based Reasoning [2]. However, as it is argued in [3] there are certain advantages in respect with rule based systems, such as the fact that users are keen to accept solutions from analogy based techniques, rather than solutions derived from uncomfortable chains of rules or neural nets.

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