By Gavin C. Cawley (auth.), Jaakko Hollmén, Frank Klawonn, Allan Tucker (eds.)
This publication constitutes the refereed court cases of the eleventh overseas convention on clever facts research, IDA 2012, held in Helsinki, Finland, in October 2012. The 32 revised complete papers awarded including three invited papers have been conscientiously reviewed and chosen from 88 submissions. All present facets of clever info research are addressed, together with clever aid for modeling and interpreting info from complicated, dynamical structures. The papers specialise in novel functions of IDA innovations to, e.g., networked electronic info structures; novel modes of information acquisition and the linked concerns; robustness and scalability problems with clever facts research concepts; 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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Extra resources for Advances in Intelligent Data Analysis XI: 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings
This results in k ∗k reﬁnements in total. From those, the top k reﬁnements are selected, resulting in k new temporary covers C1 , · · · , Ck . The choice of parameter k depends on the available compute resources. If k parallel resources (cores, computers) are available the running time of the algorithm will remain the same as the one for the simple greedy algorithm. As we will see later, the quality of the solutions will increase with larger k, due to more options being explored. Deepened Set Covering.
Return to step 2. Weighting Features for Partition around Medoids 37 The PAM algorithm inherits some of the weaknesses of K-means, including the necessity of knowing K beforehand, the high dependence on the initial medoids, and its uniform treatment of all features of the entities in the dataset I. In this paper we assume K to be known, but deal with the other weaknesses. We intuitively believe that PAM should beneﬁt from feature weighting, so that less relevant features have a lower impact on the ﬁnal set of clusters.
2 Deepening The second approach, called Deepening focuses on reducing the impact of the selection heuristic: m = sdeep (r(m)) . In contrast to the widening approach, here only the selection function changes. For example, with regard to a decision tree: in order to determine the best split at each step only the immediate splits are considered. As we will describe in the next section, one could imagine looking further ahead to better estimate the quality of each split. In eﬀect, the idea is to look forward and subsequently investigate how future steps of the reﬁnement and selection process will be aﬀected by the current choice.