Cluster Analysis and Factor Analysis

Sharma, Subhash; Kumar, Ajith

doi:10.4135/9781412973380.n18

Cited by 25 publications

(15 citation statements)

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“…To determine the number of clusters, measures of cluster homogeneity (Root Mean Square Standard Deviation and semi-partial R squared) and measures of cluster heterogeneity (Partial R squares) were examined (for more detailed discussion of these measures, see e.g. Sharma and Kumar, 2006). On the basis of the inflection points in semi-partial R squared and in R squared, a four cluster solution for supermarket shopping was suggested.…”

Section: Cluster Analyses: Advantages and Disadvantages Of Online Gromentioning

confidence: 99%

Online and store patronage: a typology of grocery shoppers

Harris

Riley

et al. 2017

IJRDM

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Purpose: Grounded on approach/avoidance behaviour theory, this study develops a typology of grocery shoppers based on the concomitant perceived advantages and disadvantages of shopping online and in store for a single cohort of consumers who buy groceries in both channels. Methodology:A survey design was employed using a sample of 871UK shoppers who had purchased groceries online and offline. The survey instrument contained items that measured the perceived advantages and disadvantages of grocery shopping online, and items relating to the perceived advantages and disadvantages of grocery shopping in traditional supermarkets. Items were selected from the extant literature and subjected to content and face validity checks. Cluster analysis was used to develop typologies of online and offline grocery shoppers. The inter-relation between the two typology sets was then examined. Findings:The results of the research provide several insights into the characteristics, perceptions and channel patronage preferences of grocery shoppers. In particular, profiling e-grocery shoppers on the basis of their concomitant perceptions of shopping online and in store suggests that the choice of whether to shop online or in store may be driven not by the perceived advantages of one channel versus the other, but by the desire to avoid the greater disadvantages of the alternative. These perceptions differ somewhat between different consumer groups.Originality/value: This study makes a noteworthy contribution to the Internet and general shopping literature by providing a profile of grocery shoppers based on their concomitant and often conflicting perceived advantages and disadvantages of shopping online and their perceived advantages and disadvantages of shopping in traditional supermarkets. The use of a single cohort of consumers overcomes the bias in previous studies that employ separate cohorts of online and offline shoppers and reveal important insights into the complex perceptions and behaviours of multi-channel grocery shoppers.

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Section: Cluster Analyses: Advantages and Disadvantages Of Online Gromentioning

confidence: 99%

Online and store patronage: a typology of grocery shoppers

Harris

Riley

et al. 2017

IJRDM

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show abstract

“…In addition, to determine the appropriate number of clusters, it is also important to take other criteria into consideration such as interpretability and usefulness of the cluster solutions [39].…”

Section: E Validation Of the Clusteringmentioning

confidence: 99%

Noninvasive Clinical Assessment of Trunk Deformities Associated With Scoliosis

Séoud

Dansereau

Cheriet

2013

IEEE J. Biomed. Health Inform.

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“…As a result of this loop we may notice that the k centroids change their location step by step until no more changes are done. In other words centroids do not move any more [4].…”

Section: K-means Clusteringmentioning

confidence: 99%

Database Clustering using Intelligent Techniques

Abbas

Sadeq²

2017

JNUS

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Owning to the huge amounts of data collected in database, cluster analysis has recently become a highly active topic in data mining research. In data mining, efforts have focused on finding methods for efficient and effective cluster analysis in large database. This paper proposes two new partitioning cluster methods, first is modified k-mean clustering algorithm with variable Neighborhood Search as a metaheuristic search and the second is modified k-mean clustering algorithm with cuckoo search as swarm intelligence.The proposed algorithms does not need to enter the value of cluster points, instead of that it finds it automatically to get the best clustering using the clustering validity. This represents its fundamental characteristic.The experiments were made on a many different sizes of databases some of the obtained from University of California (UC) Irvine Machine Learning Repository which maintain 246 data sets as a service to the machine learning community.From these experiments, it is concluded that these methods reduced the time which needed to get the best solution as a half time which needed to perform same actions and in the same time it reduced the iterations to get the best solution. In addition, these proposed clustering methods give best quality (as performance) compared with other clustering methods; the performance was improved between (10% -20%) compared with the original k-mean clustering method.

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Cluster Analysis and Factor Analysis

Cited by 25 publications

References 0 publications

Online and store patronage: a typology of grocery shoppers

Online and store patronage: a typology of grocery shoppers

Noninvasive Clinical Assessment of Trunk Deformities Associated With Scoliosis

Database Clustering using Intelligent Techniques

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