Search Results - subject:"number of clusters"

Wild Bootstrap Inference for Wildly Different Cluster Sizes

MacKinnon, James G.; Webb, Matthew D. - Economics Department, Queen's University - 2015

The cluster robust variance estimator (CRVE) relies on the number of clusters being large. A shorthand "rule of 42 … clusters is treated. We explain why this happens, study the ``effective number'' of clusters, and simulate placebo laws with … critical values based on the wild cluster bootstrap performs much better. However, this procedure fails when a small number of …

Persistent link: https://www.econbiz.de/10011185158

Wild Bootstrap Inference for Wildly Different Cluster Sizes

MacKinnon, James G.; Webb, Matthew D. - 2014

The cluster robust variance estimator (CRVE) relies on the number of clusters being large. The precise meaning of …

Persistent link: https://www.econbiz.de/10010368290

High-dimensional text datasets clustering algorithm based on cuckoo search and latent semantic indexing

Ishak-Boushaki, Saida; Kamel, Nadjet; Bendjeghaba, Omar - In: Journal of information & knowledge management : JIKM 17 (2018) 3, pp. 1850033-1-24

Persistent link: https://www.econbiz.de/10011932405

Multi-Attribute Utility Theory Based K-Means Clustering Applications

Ma, Jungmok - In: International Journal of Data Warehousing and Mining (IJDWM) 13 (2017) 2, pp. 1-12

incorporating user preferences. Using MAUT, the decision maker's value structure for the number of clusters and other attributes can …One of major obstacles in the application of the k-means clustering algorithm is the selection of the number of … clusters k. The multi-attribute utility theory (MAUT)-based k-means clustering algorithm is proposed to tackle the problem by …

Persistent link: https://www.econbiz.de/10012044225

Albaum, Gerald S.; Kara, Ali; Carmone, Frank J. - In: Journal of euromarketing 26 (2017) 3/4, pp. 81-96

Persistent link: https://www.econbiz.de/10011878160

Firm-Worker Matching in Industrial Clusters

Figueiredo, Octávio; Guimarães, Paulo; Woodward, … - Forschungsinstitut zur Zukunft der Arbeit <Bonn> - 2011

In this paper we use a novel approach and a large Portuguese employer-employee paneldata set to study the hypothesis that industrial agglomeration improves the quality of the firmworkermatching process. Our method makes use of recent developments in the estimationand analysis of models with...

Persistent link: https://www.econbiz.de/10009522215

Standortvorteil Nordwestdeutschland?Eine Untersuchung zum Einfluss von Netzwerk‐ und Clusterstrukturen in der Schweinefleischerzeugung

Deimel, Mark; Theuvsen, Ludwig - Universität <Göttingen> / Department für … - 2010

Wirft man einen Blick auf die Veränderungen der Agrarstrukturen in den letzten Dekaden,so fällt einem unweigerlich die Entwicklung Nordwestdeutschlands auf. Auseinem Gebiet, zum Teil geprägt durch „Ungunsträume“ und Selbstversorgungsstrukturen(Windhorst und Klohn, 1995; Campos, 1992),...

Persistent link: https://www.econbiz.de/10009129508

Zur Bedeutung deslandwirtschaftlichen Clustersfür ländliche Räume

Dannenberg, Peter; Kulke, Elmar - Universität <Berlin, Humboldt-Universität> / … - 2005

In der Humangeographie gewinnen die Konzepte zu regionalen Branchenclustern immergrößere Bedeutung. In diesen Konzepten arbeiten Unternehmen aus der gleichen oderähnlichen Wertschöpfungsketten zusammen in einer Region mit identischem sozialem undkulturellem Hintergrund. Aufgrund ihrer...

Persistent link: https://www.econbiz.de/10009305156

On the number of groups in clustering

Fischer, Aurélie - In: Statistics & Probability Letters 81 (2011) 12, pp. 1771-1781

Clustering is the problem of partitioning data into a finite number k of homogeneous and separate groups, called clusters. A good choice of k is essential for building meaningful clusters. In this paper, this task is addressed from the point of view of model selection via penalization. We design...

Persistent link: https://www.econbiz.de/10010576135

MCS: A Method for Finding the Number of Clusters

Albatineh, Ahmed; Niewiadomska-Bugaj, Magdalena - In: Journal of Classification 28 (2011) 2, pp. 184-209

Persistent link: https://www.econbiz.de/10009324805