By Gavin C. Cawley (auth.), Jaakko Hollmén, Frank Klawonn, Allan Tucker (eds.)
This booklet constitutes the refereed court cases of the eleventh foreign convention on clever info research, IDA 2012, held in Helsinki, Finland, in October 2012. The 32 revised complete papers provided including three invited papers have been conscientiously reviewed and chosen from 88 submissions. All present elements of clever info research are addressed, together with clever help for modeling and reading facts from advanced, dynamical platforms. The papers specialize in novel purposes of IDA thoughts to, e.g., networked electronic details structures; novel modes of knowledge acquisition and the linked matters; robustness and scalability problems with clever info research ideas; and visualization and dissemination results.
Read Online or Download Advances in Intelligent Data Analysis XI: 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings PDF
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Additional resources for Advances in Intelligent Data Analysis XI: 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings
Siebes Computational Structure While perhaps not immediately obvious, our structure function – indeed, our complete approach – ﬁts nicely in the compression for knowledge approach of Algorithmic Information Theory. For, we encode Q by EG using the cover function. Hence, by choosing suitable code words, we are compressing Q much as we did previously for the Krimp algorithm . There is a major diﬀerence with AIT, though. We do not consider all programs. There are resource bounded variants of Kolmogorov complexity  in AIT, but we are even stricter than that.
We transformed these features into numerical features by creating a binary dummy feature for each of the existing categories. C. de Amorim and T. Fenner and zero otherwise. At the end, the original categorical features were removed and each dummy features had its values reduced by their average. All other features were standardized by Equation (6). 5 ∗ range(Iv ) (6) where I¯v represents the average of feature v over the whole dataset I. This process is described in more details in . We chose it because of our previous success in utilizing it for K-Means [8, 22, 13].
The number of sets in the ﬁnal cover that each of the algorithm returns. The focus of our experiments lies on how the quality of the solutions changes, as we vary the number of used parallel resources. To evaluate the average performance of the algorithms with respect to the parallelization parameter, for each data set each algorithm was run randomized 50 times and the mean and the standard deviation of the size of the cover was reported. If our initial hypothesis is correct, the average performance should go up (here: the size of the cover should go down) when more parallel resources are used.