Cluster Analysis

By Kimmo Vehkalahti, Brian S. Everitt. Science, 2002

6,255 citations recorded by the source corpus.

ەی اكتره . Abstract This study was conducted with the aim to identify the most important effective factors that lead to increase the car accidents in Erbil Governorate, by classifying the variables to the homogeneous groups, and identifying the most important variables, also to find the effective of the variables in the studied phenomenon. Later two types of Multivariate Analysis; the Factor Analysis by Using Principle components method and Cluster Analysis by Using Ward method have been applied as a tools to analyzing the data, then the comparison was made between the two analysis . The most important result for this study is, classifying the variables that caused the increasing of car accidents to eight significant factors and explained the largest part of the variance

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/909e386bb9513ad46b38bb01d1327c121b6ba55e

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In Artificial Intelligence

Citations in the corpus

Analysing behavioural differences of farm households: An example of income diversification strategies based on European farm survey data (2017, methodology): “…with factor analysis is an explorative structureseeking method to detect patterns in a population based on a predefined statistical measure of distance (Aldenderfer and Blashfield, 1984) and thus has a relatively high objectivity assigning farms to types compared to other assignment measures.”
Clustering by Scale-Space Filtering (2000, background): “Therefore, each blob is a cluster as defined in [31].”
SPSS TwoStep Cluster - a first evaluation (2004, methodology): “Simulation studies suggest, that z-standardization is ineffective (for a summary of simulation results; see Everitt et al. 2001: 51).”
Athlete leadership dispersion and satisfaction in interactive sport teams. (2007, background): “(2004) and Aldenderfer and Blashfield (1984). Based on the inspection of a graphical representation of potential group membership (i.”
Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, methodology): “Hierarchical clustering method can be divided into two types: divisive and agglomerative (Everitt et al. 2001).”
The Impact of Ignoring a Level of Nesting Structure in Multilevel Growth Mixture Models: A Monte Carlo Study (2010, methodology): “As a model-based clustering approach, FMM differs from standard cluster analysis (Everitt et al., 2001).”
Beyond resilients, undercontrollers, and overcontrollers? an extension of personality prototype research (2006, background): “Sample size and sample composition as crucial issues in prototype research Cluster analysis is known for its sensitivity to sample size and sample composition (Aldenderfer & Blashfield, 1996).”
How does gene expression clustering work? (2005, background): “If you’re interested in delving deeper, see Jain and Dubes 2 for a comprehensive overview of clustering algorithms or Aldenderfer and Blashfield 3 for a more concise treatment.”
Proposed minimum reporting standards for data analysis in metabolomics (2007, methodology): “There are several excellent reviews on current multivariate techniques (Beebe et al. 1998; Chatfield and Collins 1980; Duda et al. 2001; Everitt 1993; Krzanowski 1988; Lavine 1998; Manly 1994; Martens and Næs 1989; Massart et al. 1997).”
Managing the Transition to College: Family Functioning, Emotion Coping, and Adjustment in Emerging Adulthood (2011, positive): “Cluster analysis is a multivariate approach allowing for the simultaneous consideration of a participants’ configuration of ratings across multiple dimensions of emotion coping (Alexander & Blashfield, 1984).”
Exploring the Relationship Between a Multidimensional and Multifaceted Burnout Concept and Self-Rated Performance (2005, methodology): “This is another test of the generality of the cluster solution against relevant criteria (Aldenderfer & Blashfield, 1984).”
Iterative shrinking method for clustering problems (2006, methodology): “We propose an alternative to the merge-based approach by removing the clusters iteratively one by one until the desired number of clusters is reached.”
Fast algorithm for detecting community structure in networks. (2003, methodology): “Our algorithm falls in the general category of agglomerative hierarchical clustering methods [15, 16].”