Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th Birthday

Vol. 92. Берлин : Springer, 2014.

Научный редактор: Алескеров Ф. Т., Гольденгорин Б. И., Пардалос П. О.

The volume is dedicated to Boris Mirkin on the occasion of his 70th birthday. In addition to his startling PhD results in abstract automata theory, Mirkin’s ground breaking contributions in various fields of decision making and data analysis have marked the fourth quarter of the 20th century and beyond. Mirkin has done pioneering work in group choice, clustering, data mining and knowledge discovery aimed at finding and describing non-trivial or hidden structures—first of all, clusters, orderings, and hierarchies—in multivariate and/or network data.

This volume contains a collection of papers reflecting recent developments rooted in Mirkin's fundamental contribution to the state-of-the-art in group choice, ordering, clustering, data mining, and knowledge discovery. Researchers, students, and software engineers will benefit from new knowledge discovery techniques and application directions.

Главы книги

A Pseudo-Boolean Approach to the Market Graph Analysis by Means of the P-Median Model

Гольденгорин Б. И., Кочетуров А. А., Пардалос П. О., , in: Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th BirthdayVol. 92.: Berlin: Springer, 2014. P. 77–89.

In the course of recent ten years algorithms and technologies for network structures analysis have been applied to financial markets among other approaches. The first step of such an analysis is to describe the considered financial market via the correlation matrix of stocks prices over a certain period of time. The second step is to ...

Добавлено: 7 октября 2013 г.

The manipulability index in the IANC model

Веселова Ю. А., , in: Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th BirthdayVol. 92.: Berlin: Springer, 2014. P. 391–404.

Procedures aggregating individual preferences into a collective choice differ in their vulnerability to manipulations. To measure it, one may consider the share of preference profiles where manipulation is possible in the total number of profiles, which is called Nitzan-Kelly's index of manipulability. The problem of manipulability can be considered in different probability models. There are ...

Добавлено: 24 октября 2013 г.

Least-squares consensus clustering versus: (a) other consensus approaches and (b) k-means

Миркин Б. Г., Andrey Shestakov, , in: Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th BirthdayVol. 92.: Berlin: Springer, 2014.

We develop a consensus clustering framework proposed three decades ago in Russia and experimentally demonstrate that our least squares consensus clustering algorithm consistently outperforms several recent consensus clustering methods. ...

Добавлено: 4 ноября 2013 г.

An AST method for scoring string-to-text similiarity in semantic text analysis

Миркин Б. Г., Артемова Е. Л., , in: Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th BirthdayVol. 92.: Berlin: Springer, 2014.

Abstract. A suffix-tree based method for measuring similarity of a key phrase to an unstructured text is proposed. The measure involves less computation and it does not depend on the length of the text or the key phrase. This applies to the following tasks in semantic text analysis: Finding interrelations between key phrases over a set of ...

Добавлено: 4 ноября 2013 г.

A Note on the Effectiveness of the Least Squares Consensus Clustering

Миркин Б. Г., Шестаков А. В., , in: Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th BirthdayVol. 92.: Berlin: Springer, 2014.

Добавлено: 23 января 2015 г.

Selecting the Minkowski Exponent for Intelligent K-Means with Feature Weighting

Миркин Б. Г., Amorim R., , in: Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th BirthdayVol. 92.: Berlin: Springer, 2014. P. 1250–1257.

Recently, a three-stage version of K-Means has been introduced, at which not only clusters and their centers, but also feature weights are adjusted to minimize the summary p-th power of the Minkowski p-distance between entities and centroids of their clusters. The value of the Minkowski exponent p appears to be instrumental in the ability of the method to recover ...

Добавлено: 16 марта 2015 г.

Ключевые слова: Cluster, data analysis, mathematical programming, operations research, theory of computing, theory of data, artificial intelligence