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Machine Theory

Artificial Intelligence and Soft Computing: 15th by Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard

By Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada

The two-volume set LNAI 9692 and LNAI 9693 constitutes the refereed lawsuits of the fifteenth foreign convention on man made Intelligence and tender Computing, ICAISC 2016, held in Zakopane, Poland in June 2016.
The 134 revised complete papers provided have been conscientiously reviewed and chosen from 343 submissions. The papers incorporated within the first quantity are equipped within the following topical sections: neural networks and their functions; fuzzy structures and their functions; evolutionary algorithms and their functions; agent structures, robotics and keep an eye on; and trend category. the second one quantity is split within the following components: bioinformatics, biometrics and scientific purposes; information mining; man made intelligence in modeling and simulation; visible details coding meets laptop studying; and diverse difficulties of synthetic intelligence.

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Extra resources for Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part II

Example text

Every couple (i, c) ∈ R, where i ∈ I and c ∈ C, means that the instance i belongs to the cluster c. Consider for example a dataset of nine instances D = {1, 2, 3, 4, 5, 6, 7, 8, 9} partitioned using five base clusterings into the five following partitions: P 1 = {{1, 2, 3}, {4, 5, 6, 7, 8, 9}}, P 2 = {{1, 2, 3}, {4, 5, 6, 7, 8, 9}}, P 3 = {{1, 2, 3, 4, 5}, {6, 7}, {8, 9}}, P 4 = {{4, 5, 6, 7}, {1, 2, 3}, {8, 9}}, and P 5 = {{4, 5, 6, 7}, {1, 2, 3}, {8, 9}}. Table 1 shows the resulting cluster membership matrix consisting of 9 rows (instances) and 14 columns (total number of clusters in base clusterings).

2). It may also highlight groups of instances that are far from being similar to other instances, such as the column of stable cluster circled in red and the similar column beside. Furthermore, the fact that these two columns merge into one cluster, circled in green, rather than merging with any of the previous stable clusters (circled in blue) provides more insight on the peculiar information they hold. The ST value can also point to the result that is more stable than others (the consensus for DT = 6), but as the clustering task is more related to the relevance of the found patterns to user preferences, the user may prefer to select the consensus at DT = 7 Fig.

The first consensus consists of instance sets of FCPs that define clustering patterns common to all base clusterings (lines 5–7 in Algorithm 1), or data fragments [21]. Consensuses are then iteratively built using results of the previous consensus (lines 9–32 in Algorithm 1) according to the following properties of instance sets. At each DT, an instance set I ⊆ I has one of the following three properties: (i) Uniqueness: It does not intersect with any other set I ⊆ I, that is, I∩I = ∅. (ii) Inclusion: It is a subset of another set I ⊆ I, that is, I ⊆ I .

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