2017-03-21 software

stringi 1.1.3 released

I have submitted a new (bugfix) release of the stringi package to CRAN.


* [REMOVE DEPRECATED] `stri_install_check()` and `stri_install_icudt()`
marked as deprecated in `stringi` 0.5-5 are no longer being exported.

* [BUGFIX] #227: Incorrect behavior of `stri_sub()` and `stri_sub<-()`
if the empty string was the result.

* [BUILD TIME] #231: The `./configure` (*NIX only) script now reads the
following environment varialbes: `STRINGI_CFLAGS`, `STRINGI_CPPFLAGS`,
see `INSTALL` for more information.

* [BUILD TIME] #253: call to `R_useDynamicSymbols` added.

* [BUILD TIME] #230: icudt is now being downloaded by
`./configure` (*NIX only) *before* building.

* [BUILD TIME] #242: `_COUNT/_LIMIT` enum constants have been deprecated
as of ICU 58.2, stringi code has been upgraded accordingly.
2017-03-15 new paper

FUZZ-IEEE'17: Two Papers Accepted

Two papers I co-author have been accepted for publication in Proceedings of the FUZZ-IEEE'17 conference that will be held in Naples, Italy.
  • Bartoszuk M., Gagolewski M., Binary aggregation functions in software plagiarism detection
  • Cena A., Gagolewski M., OWA-based linkage and the Genie correction for hierarchical clustering
2016-12-12 new paper

Penalty-Based Aggregation of Multidimensional Data

My paper Penalty-Based Aggregation of Multidimensional Data has been accepted for publication in Fuzzy Sets and Systems (Special Issue on Aggregation Functions).
Abstract. Research in aggregation theory is nowadays still mostly focused on algorithms to summarize tuples consisting of observations in some real interval or of diverse general ordered structures. Of course, in practice of information processing many other data types between these two extreme cases are worth inspecting. This contribution deals with the aggregation of lists of data points in Rd for arbitrary d≥1. Even though particular functions aiming to summarize multidimensional data have been discussed by researchers in data analysis, computational statistics and geometry, there is clearly a need to provide a comprehensive and unified model in which their properties like equivariances to geometric transformations, internality, and monotonicity may be studied at an appropriate level of generality. The proposed penalty-based approach serves as a common framework for all idempotent information aggregation methods, including componentwise functions, pairwise distance minimizers, and data depth-based medians. It also allows for deriving many new practically useful tools.
2016-11-21 new book

Przetwarzanie i analiza danych w języku Python

My book on Python for Data Processing and Analysis is now available in Polish book stores.
Przetwarzanie i analiza danych w języku Python - okładka
2016-11-21 new book

Programowanie w języku R (2nd Ed., revised and extended)

The 2nd edition of my R Programming Book is now available in Polish book stores.
Programowanie w języku R - okładka

Eusflat'17 Special Session:
Algorithms for Data Aggregation and Fusion

Call for contributions – EUSFLAT 2017 (10th Conference of the European Society for Fuzzy Logic and Technology, Warsaw, Poland) Special Session Algorithms for Data Aggregation and Fusion; for more details, click here.
2016-10-27 new paper

Penalty-Based and Other Representations of Economic Inequality

My paper with Gleb Beliakov and Simon James, entitled Penalty-based and other representations of economic inequality, has been accepted for publication in International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems today.
Abstract. Economic inequality measures are employed as a key component in various socio-demographic indices to capture the disparity between the wealthy and poor. Since their inception, they have also been used as a basis for modelling spread and disparity in other contexts. While recent research has identified that a number of classical inequality and welfare functions can be considered in the framework of OWA operators, here we propose a framework of penalty-based aggregation functions and their associated penalties as measures of inequality.
2016-10-14 invited talk

Invited Talk @ European R Users Meeting 2016

Today I gave an invited talk (Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm and its R interface) at the European R Users Meeting that is held in Poznań, Poland.
Abstract. The time needed to apply a hierarchical clustering algorithm is most often dominated by the number of computations of a pairwise dissimilarity measure. Such a constraint, for larger data sets, puts at a disadvantage the use of all the classical linkage criteria but the single linkage one. However, it is known that the single linkage clustering algorithm is very sensitive to outliers, produces highly skewed dendrograms, and therefore usually does not reflect the true underlying data structure - unless the clusters are well-separated.
To overcome its limitations, we proposed a new hierarchical clustering linkage criterion called genie. Namely, our algorithm links two clusters in such a way that a chosen economic inequity measure (e.g., the Gini or Bonferroni index) of the cluster sizes does not increase drastically above a given threshold.
Benchmarks indicate a high practical usefulness of the introduced method: it most often outperforms the Ward or average linkage in terms of the clustering quality while retaining the single linkage speed. The algorithm is easily parallelizable and thus may be run on multiple threads to speed up its execution further on. Its memory overhead is small: there is no need to precompute the complete distance matrix to perform the computations in order to obtain a desired clustering. In this talk we will discuss its reference implementation, included in the genie package for R.