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The main aim of “Knowledge Discovery in Databases” is to extract and interpret interesting patterns present in real-world datasets. Measures to identify interesting patterns (called Interestingness Measures) may be categorized on the basis of statistical significance (Objective Measures), or on the basis of data and subjectivity of the user (Subjective Measures) which includes user’s domain knowledge. Three major steps in dealing with subjective measures are (1) knowledge acquisition from the user in terms of his beliefs, (2) the matching methodology for comparing generated association rules and user’s belief, and (3) generation of interesting rules that may be unexpected, novel or actionable. We propose and construct a theoretical framework for studying subjective interestingness in association rule mining, which takes care of these steps. We attempt to fit prior work done on subjective interestingness into this framework, thus identifying relevant research gaps. The notion of subjective interestingness confines to knowledge discovery by managers in a supermarket focusing on their expectations based on the data available. Perceptions behind customer purchases are not explicitly considered. We pose a major research question in subjective interestingness: What is the nature of subjective interestingness among associations of items, in terms of manager’s expectations and customers’ purchase patterns?

The ubiquitous low-cost connectivity synonymous with the internet has changed the competitive business environment by dissolving traditional sources of competitive advantage based on size, location and the like. In this level playing field, firms are forced to compete on the basis of knowledge. Data mining tools and techniques provide e-commerce applications with novel and significant knowledge. This knowledge can be leveraged to gain competitive advantage. However, the automated nature of data mining algorithms may result in a glut of patterns — the sheer numbers of which contribute to incomprehensibility. Importance of automated methods that address this immensity problem, particularly with respect to practical application of data mining results, cannot be overstated. We first examine different approaches to address this problem citing their applicability to e-commerce whenever appropriate. We then provide a detailed survey of one important approach, namely interestingness measure, and discuss its relevance in e-commerce applications such as personalization in recommender systems. Study of current literature brings out important issues that reveal many promising avenues for future research. We conclude by reiterating the importance of post-processing methods in data mining for effective and efficient deployment of e-commerce solutions.

Metal–Insulator–Metal structure was used to investigate the dielectric, thermally stimulated discharge current(TSDC)and pyroelectric behavior of poly methyl methacrylate(PMMA)thin films prepared by isothermal immersion method. The surface morphology and the structure of the above said films were studied by using atomic force microscopy(AFM)and X-ray diffraction(XRD)respectively. The XRD spectrum of as grown and films annealed at 513 K indicated the amorphous nature. No pits and dendritic features were observed from the AFM spectrum. The only important topographic features observed is the hillocks of about 10–12 nm large with a peak to valley vertical distance of about 0.5–1 nm. Both as grown and films annealed at 513 K showed very smooth surface and amorphous nature. From the TSDC spectrum activation energy(E), capture cross-section(σ), escape frequency(ν)and relaxation time(τ)were calculated. The observed amorphous phase, low loss, dielectric and thermal behavior imply the feasibility of utilizing PMMA t...

In this paper, we discuss the applicability of qualitative and quantitative reasoning techniques to study the process of Organizational Learning. We have used cognitive maps of a company (for the past five years) taken from the Indian automobile industry to understand the Organizational Learning process. We have conducted stochastic simulation experiment on an uncertainty-based cognitive map (the latest year). We generated scenarios for the future and analysed each scenario with respect to data obtained from the past five-years cognitive maps, in light of the theory on Organizational Learning.

Electronic Systems are the core of everyday lives. They form an integral part in financial networks, mass transit, telephone systems, power plants and personal computers. Electronic systems are increasingly based on complex VLSI (Very Large Scale Integration) integrated circuits. Initial electronic design automation is concerned with the design and production of VLSI systems. The next important step in creating a VLSI circuit is Physical Design. The input to the physical design is a logical representation of the system under design. The output of this step is the layout of a physical package that optimally or near optimally realizes the logical representation. Physical design problems are combinatorial in nature and of large problem sizes. Darwin observed that, as variations are introduced into a population with each new generation, the less-fit individuals tend to extinct in the competition of basic necessities. This survival of fittest principle leads to evolution in species. The objective of the Genetic Algorithms (GA) is to find an optimal solution to a problem .Since GA-s are heuristic procedures that can function as optimizers, they are not guaranteed to find the optimum, but are able to find acceptable solutions for a wide range of problems. This survey paper aims at a study on Efficient Algorithms for VLSI Physical design and observes the common traits of the superior contributions., {"references":["Palesi Maurizio and Tony Givargis, \"Multi-Objective Design Space\nExploration Using Genetic Algorithms\", Proceedings of the\n10thInternational Symposium on Hardware/software Codesign, ACM\nPress,Estes Park, Colorado, pp 67-72, 2002.","W. Tan, et al., \"An efficient multi-way algorithm for balance partitioning\nof VLSI Circuits\", IEEE International Conference on Computer Design,\npp 608-613, 1997.","Schnecke V., Vornberger O (1997) Hybrid Genetic Algorithms for\nConstrained Placement Problems. IEEE Transactions on Evolutionary\nComputation. Vol. I. No.4. :266-277.","Sandeep Singh Gill, Rajeevan Chandel, and Ashwani Chandel , \"\nComparative study of Ant Colony and Genetic Algorithms for VLSI\ncircuit partitioning\" , International Journal of Electrical and Computer\nEngineering 4:6 2009","P. Mazumder, E.M. Rudnik, \"Genetic Algorithms for VLSI Design,\nLayout and Test Automation\", Pearson Education, 2003.","A. E. Caldwell, A. B. Kahng and I. L. Markov, \"Can Recursive\nBisection Alone Produce Routable Placements?\", In Proc. Design\nAutomation Conf, pp. 477- 482, 2000.","G. Karypis and V. Kumar, \"Multilevel k-way hypergraph","partitioning,\" In Proc. Design Automation Conf, pp. 343-348, 1998.","G. Karypis, R. Aggarwal, V. Kumar and S. Shekhar, \"Multilevel\nHypergraph Partitioning: Applications in VLSI Domain,\" In Proc.\nDesign Automation Conf, pp. 526-529, 1997.\n[10] Chang-Tzu Lin, De-Sheng Chen and Yi-Wen Wang, \"An Efficient\nGenetic Algorithm for Slicing Floorplan Area Optimization,\" In Proc.\nInt.Symp. on Circuits and Systems, pp.879 -882, 2002.\n[11] Chang-Tzu Lin, De-Sheng Chen and Yi-Wen Wang, \"An Efficient\nGenetic Algorithm for Slicing Floorplan Area Optimization,\" In Proc.\nInt.Symp. on Circuits and Systems, pp.II-879 -II-882, 2002.\n[12] Jens Lienig, James P.Cohoon, \"Lecture notes in computer science\n\",volume-1141,Parallel Problem Solving from NatureÔÇöPPSN IV , In\npp. 839-848,1996.\n[13] Sathyamoorthy S, Andaljayalakshmi G.,\"Hybrid Genetic algorithm For\nVLSI Macro Cell layout\",In 6th online World conference on soft\ncomputing in industrial applications, September 10-24,2001.\n[14] Sandeep sign Gill, Dr.Rajeevan Chandel, Dr.Ashwani Chandel,\"Genetic\nAlgorithm Based Approach To circuit Partioning\",International Journal\nof computer and electrical engineering ,Vol. 2,No. 2, April,2010.\n[15] D.E. Goldberg, \"Genetic Algorithms in Search, Optimization and\nMachine learning\", Pearson Education, 2004.\n[16] http://www.gigascale.org/bookshelf\n[17] Chang-Tzu Lin, De-Sheng Chen and Yi-Wen Wang, \"Robust Fixedoutline\nFloorplanning Through Evolutionary Search,\" In Proc. Asia and\nSouth Pacific on Design Automation Conf, pp. 42-44, 2004.\n[18] Saurabh N. Adya and Igor L. Markov, \"Consistent Placement of Macro-\nBlocks Using Floorplanning and Standard-Cell Placement,\" In Proc. Int.\nSymp. on Physical Design, pp. 12-17, 2002\n[19] S.-Y. Ho, and X.-I. Chang, \"An efficient generalized multiobjective\nevolutionary algorithm,\" In Proc. Genetic and Evolutionary Computation\nConference, pp. 871-878, 1999"]}

Knowledge management (KM) transforms a firm’s knowledge-based resources into a source of competitive advantage. Knowledge creation, a KM process, deals with the conversion of tacit knowledge to explicit knowledge and moving knowledge from the individual level to the group, organizational, and interorganizational levels (Alavi & Leidner, 2001). Four modes?namely, socialization, externalization, combination, and internalization?create knowledge through the interaction and interplay between tacit and explicit knowledge. The “combination” mode consists of combining or reconfiguring disparate bodies of existing explicit knowledge (like documents) that lead to the production of new explicit knowledge (Choo, 1998). Transactional databases are a source of rich information about a firm’s processes and its business environment. Knowledge Discovery in Databases (KDD), or data mining, aims at uncovering trends and patterns that would otherwise remain buried in a firm’s operational databases. KDD is “the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.” (Fayyad, Piatetsky-Shapiro, & Smyth, 1996). KDD is a typical example of IT-enabled combination mode of knowledge creation (Alavi & Leidner, 2001).

Knowledge management (KM) transforms a firm’s knowledge-based resources into a source of competitive advantage. Knowledge creation, a KM process, deals with the conversion of tacit knowledge to explicit knowledge and moving knowledge from the individual level to the group, organizational, and interorganizational levels (Alavi & Leidner, 2001). Four modes¾namely, socialization, externalization, combination, and internalization¾create knowledge through the interaction and interplay between tacit and explicit knowledge. The “combination” mode consists of combining or reconfiguring disparate bodies of existing explicit knowledge (like documents) that lead to the production of new explicit knowledge (Choo, 1998). Transactional databases are a source of rich information about a firm’s processes and its business environment. Knowledge Discovery in Databases (KDD), or data mining, aims at uncovering trends and patterns that would otherwise remain buried in a firm’s operational databases. KDD is “the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.” (Fayyad, Piatetsky-Shapiro, & Smyth, 1996). KDD is a typical example of IT-enabled combination mode of knowledge creation (Alavi & Leidner, 2001).

In this paper, the authors analyze the loan evaluation problem in India. They give a hierarchical decomposition of this problem which facilitates a functional fit of suitable architectures. After motivating the need for a neural approach, they propose a mixed-network architecture that consists partly of expert-system and partly of hybrid neural net independently and justify the need for such a mixed approach at appropriate places.

A new class of concepts called 'Conceptual Transformers' is defined and illustrated with the help of examples. The knowledge structure to represent this class of concepts is detailed. The role of these transformers, in clustering of objects using a semantic measure is looked into. Salient features of this class are examined and illustrated. An example from real-world to depict Transformer-based Semantic Clustering is given.