Multidimensional data have various representations. Thanks to their simplicity in modeling multidimensional data and the availability of various mathematical tools (such as tensor decompositions) that support multi-aspect analysis of such data, tensors are increasingly being used in many application domains including scientific data management, sensor data management, and social network data an...

FCA is a mathematical formalism having many applications in data mining and knowledge discovery. Originally it deals with binary data tables. However, there is a number of extensions that enrich standard FCA. In this paper we consider two important extensions: fuzzy FCA and pattern structures, and discuss the relation between them. In particular we introduce a scaling procedure that enables rep...

FCA is a mathematical formalism having many applications in data mining and knowledge discovery. Originally it deals with binary data tables. However, there is a number of extensions that enrich standard FCA. In this paper we consider two important extensions: fuzzy FCA and pattern structures, and discuss the relation between them. In particular we introduce a scaling procedure that enables rep...

We deal with methods for analyzing complex structured data, especially, distribution valued data. Nowadays, there are many requests to analyze various types of data including spatial data, time series data, functional data and symbolic data. The idea of symbolic data analysis proposed by Diday covers a large range of data structures. We focus on distribution valued dissimilarity data and multid...

Abstract Detection and removal of outliers in a dataset is fundamental preprocessing task without which the analysis data can be misleading. Furthermore, existence anomalies heavily degrade performance machine learning algorithms. In order to detect an unsupervised manner, some novel statistical techniques are proposed this paper. The based on methods considering compactness other properties. n...