Advances in Intelligent Data Analysis XIII: 13th by Hendrik Blockeel, Matthijs van Leeuwen, Veronica Vinciotti

By Hendrik Blockeel, Matthijs van Leeuwen, Veronica Vinciotti

This e-book constitutes the refereed convention complaints of the thirteenth overseas convention on clever information research, which used to be held in October/November 2014 in Leuven, Belgium. The 33 revised complete papers including three invited papers have been rigorously reviewed and chosen from 70 submissions dealing with all types of modeling and research tools, regardless of self-discipline. The papers conceal all features of clever information research, together with papers on clever aid for modeling and examining facts from advanced, dynamical systems.

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Additional info for Advances in Intelligent Data Analysis XIII: 13th International Symposium, IDA 2014, Leuven, Belgium, October 30 -- November 1, 2014. Proceedings (Lecture Notes in Computer Science)

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Given that the Munch algorithm runs for T, iterations, the fitness function is O(√n), and that the fitness function is where all of the computational complexity of the Hill Climbing algorithm is, then the overall complexity of the run is O(T√n). Thus, the smaller the value of T the faster the algorithm runs. Table 3 shows the time savings under each scheme compared to the full run of 10 million iterations. 88%, this is for Strategy 2. Results show that T4 has the highest percentage of saving in terms of runtime.

1029–1036 (2005) 12. : Using automatic clustering to produce high-level system organizations of source code. In: International Workshop on Program Comprehension (IWPC 1998), pp. 45–53. IEEE Computer Society Press, Los Alamitos (1998) 13. : Using Heuristic Search Techniques to Extract Design Abstractions from Source Code. In: Proc. Genetic and Evolutionary Computation Conference (GECCO 2002). Morgan Kaufmann Publishers (2002) 14. : Multivariate Analysis (Probability and Mathematical Statistics).

Fig. 7 shows a plot of HS against EVM for the whole system. To find out whether there is a relationship between HS and EVM they were correlated. 791 is produced, which indicates that the correlation is highly significant. 115. It is interesting to observe that this strong correlation illustrates the credibility of EVM as a good metric. The plot shows that EVM is a good predictor for HS. 0); since there would be no coupling. Despite the fact that EVM is not a measure of coupling or cohesion, it was still strongly correlated with HS.

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