F-Mapper: A Fuzzy Mapper clustering algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246978" target="_blank" >RIV/61989100:27240/20:10246978 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0950705119304794?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0950705119304794?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.knosys.2019.105107" target="_blank" >10.1016/j.knosys.2019.105107</a>

Jazyk výsledku

angličtina

Název v původním jazyce

F-Mapper: A Fuzzy Mapper clustering algorithm

Popis výsledku v původním jazyce

Using topology in data analysis, known as Topological Data Analysis (TDA), is now a promising new area of data mining research. One of the important and foundational tools of TDA is the Mapper algorithm. During the past two decades, this algorithm has proven its useful and robust abilities in extracting insights and meaningful information from high-dimensional datasets. Nevertheless, several alterations in the choices of parameters, such as lens, cover and clustering, can be used to develop this algorithm. In this paper, we propose the F-Mapper algorithm, based on the foundation of the Mapper algorithm, to solve the problem of automating when dividing cover intervals with an arbitrary percentage of overlap. To clarify the efficiency of this enhanced algorithm, experiments were carried out on three datasets, including the Unit Circle, Reaven and Miller Diabetes, and NKI Breast Cancer. The experimental results will be analyzed and compared with those of the original method, the Mapper algorithm, through the output image and silhouette coefficient score in the evaluation of clustering. (C) 2019 Elsevier B.V.

Název v anglickém jazyce

F-Mapper: A Fuzzy Mapper clustering algorithm

Popis výsledku anglicky

Using topology in data analysis, known as Topological Data Analysis (TDA), is now a promising new area of data mining research. One of the important and foundational tools of TDA is the Mapper algorithm. During the past two decades, this algorithm has proven its useful and robust abilities in extracting insights and meaningful information from high-dimensional datasets. Nevertheless, several alterations in the choices of parameters, such as lens, cover and clustering, can be used to develop this algorithm. In this paper, we propose the F-Mapper algorithm, based on the foundation of the Mapper algorithm, to solve the problem of automating when dividing cover intervals with an arbitrary percentage of overlap. To clarify the efficiency of this enhanced algorithm, experiments were carried out on three datasets, including the Unit Circle, Reaven and Miller Diabetes, and NKI Breast Cancer. The experimental results will be analyzed and compared with those of the original method, the Mapper algorithm, through the output image and silhouette coefficient score in the evaluation of clustering. (C) 2019 Elsevier B.V.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Knowledge-Based Systems

ISSN

0950-7051

e-ISSN

—

Svazek periodika

189

Číslo periodika v rámci svazku

FEb

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

—

Kód UT WoS článku

000510955100012

EID výsledku v databázi Scopus

2-s2.0-85073818583