On the General Distance Measure, In:

Abstract: In Walesiak [1993], pp. 44-45 the distance measure was proposed, which can be used for the ordinal data. In the paper the proposal of the general distance measure is given. This measure can be used for data measured in ratio, interval and ordinal scale. The proposal is based on the idea of the generalised correlation coefficient

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Growing Neural Gas-like Networks and their Application to Data Analysis and Clustering in Marketing

Decker R. Growing Neural Gas-like Networks and their Application to Data Analysis and Clustering in Marketing. In: Jajuga K, Walesiak M, eds. Taksonomia 15 - Klasyfikacja i analiza danych - teoria i zastosowania, Proceedings of the SKAD Conference 2007. Wroclaw: Uniwersytet Ekonomiczny we Wroclawiu; 2008: 17-34

Applying investment strategies in the capital market

Straipsnyje nagrinėjama investavimo sąvoka bei pasiūlius jos apibendrinantį apibrėžimą, pateikiama investavimo klasifikacija, nurodomi investuotojų tipai. Akcentuojama, jog pradedantieji investuotojai įžengdami į finansų pasaulį ir bandydami jį suprasti, susiduria su daugybe informacijos, kuri dažnai būna prieštaringa. Tokiems asmenims tampa sudėtinga priimti racionalius sprendimus, todėl išskiriama aktyvi ir pasyvi investavimo strategija, aprašomi šių strategijų privalumai bei trūkumai. Investicijų procesą reglamentuoja du itin svarbūs aspektai – investicijų grąža ir rizika, todėl investuojant būtina sukurti specialų veiksmų algoritmą – tikslo nustatymas, situacijos įvertinimas, resursų apskaičiavimas, galimų alternatyvų paieška. Manoma, kad investuotojų elgseną galima pagrįsti elgsenos finansų teorija, kuri šiuolaikiniame pasaulyje turi vis didesnę reikšmę. Atitinkamai, svarbu atsižvelgti į investuotojų poreikius ir finansines galimybes, todėl tyrimui atrinkti trys daugiakriteriniai vertinimo metodai SAW, TOPSIS ir VIKOR Naudojant šiuos metodus vadovaujamasi pasyvia investavimo strategija, o pasiekti rezultatai nurodo, kad pelningiausia investicija – Skyworks Solution akcijas, todėl formuojant akcijų portfelį jas reikėtų įtraukti pirmiausia.Article describes investment concept and provides classification, indicates investors’ types by providing generalized definition on investment. Pointing that rational decisions are hard to make for not – experienced/beginner investors. Therefore, passive and active investment strategies are described. Article covers their pros and cons. Risk and return are key elements within investment process. Following actions are must if aiming to invest successfully – define goal, evaluate situation, measure resources, define feasible alternatives. Investors’ behaviour could be justified by financial behaviour theory, which is highly recognizable these days. Moreover, it is important to notice investors’ requirements and financial capabilities. Best possible investment strategy selected for the research purposes and multi – criteria evaluation methods used (SAW, TOPSIS, VIKOR) defining most profitable investment model