A Comparison Between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis for Identifying Tourists Segments

Rashid, Noor Rashidah

doi:10.31436/iiumej.v12i6.199

Cited by 5 publications

(7 citation statements)

References 3 publications

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“…Again, the cluster distance for the 2 nd distance matrix is determined from the lowest value in Table 5 with C.D.2 = 1.05. For this 2 nd improvement, area A10 is grouped with area A11 and all values of Ei, (10,11) = E (10,11),i are as follows:…”

Section: Resultsmentioning

confidence: 99%

“…According to dataset of 3 variables: daily use of both languages, Cantonese proficiency and English proficiency, all people were grouped into 4 groups: group A of frequent language users with high Cantonese proficiency, group B of frequent language users with intermediate Cantonese proficiency, group C of moderate language users with high Cantonese proficiency and group D of moderate language users with high Cantonese proficiency. Noor, Sabri and Safiek [10] compared performances of Single Linkage method and Complete Linkage method by clustering 200 tourists in Kapas Island into subgroups. With these two methods, the results showed that Complete Linkage method was more proper method to analyze and group these 200 tourists than Single Linkage method since the chain effect grouped the 191 tourists into one group.…”

Section: Introductionmentioning

confidence: 99%

“…Researches [8][9][10][11][12][13][14][15][16] really show that the agglomerative hierarchical cluster methods can be applied to different types of datasets to cluster data into groups with multi-variables. However, Single Linkage method may not good to cluster dataset with closely values.…”

Section: Introductionmentioning

confidence: 99%

“…Since the secondary wind dataset [7] already informs the values of wind power in each alternative areas so that users can conveniently rank from the lowest wind energy area to the highest wind energy area, however, these datasets should be more useful if the data in this wind dataset can be clustered into groups with same wind quality in each group. Among many agglomerative hierarchical cluster methods [8][9][10][11][12][13][14][15][16], the simplest method without the effect of the closely data values and using together with other measure techniques should be Complete Linkage Method.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Quality Evaluation of Wind Energy Data with Complete Linkage Clustering

2022

IJIES

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Although wind is an important free energy and most investors or farmers would like to invest in wind energy projects, they sometimes lack of the wind quality data in alternative areas for making decision. It should be definitely good to have some simple methods to classify the quality of wind energy for alternative areas. In this study, Complete Linkage method combining with the Euclidean distance calculation, which is really a simple method for users, is introduced to cluster wind energy quality of alternative areas. In a case of 13 alternative areas in the south of Thailand, the data of average wind velocity along 12 months from the secondary data source can be used to generate the initial distance matrix before continuously improving with Complete Linkage method. Finally, these 13 alternative areas are suitable clustered at C.D. = 5.11 into 3 groups of the low wind quality area with I.D. = 1.21, the medium wind quality area with I.D. = 1.41 and the high wind quality area with I.D. = 1.45.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quality Evaluation of Wind Energy Data with Complete Linkage Clustering

2022

IJIES

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“…Single Linkage focuses on the smallest distance or the closest neighbour between sub-clusters. Complete Linkage takes the largest distance or the farthest neighbour between sub-clusters [4], while Average Linkage takes the average distance between sub-clusters. Previous research [5] conducted a hierarchical tree-based image segmentation.…”

Section: Jurnal Ilmu Komputer Dan Informasi (Journal Of Computer Sciementioning

confidence: 99%

Multi-Class Region Merging for Interactive Image Segmentation Using Hierarchical Clustering Analysis

Aisyah

Anggraini

Putriwijaya

et al. 2019

Jurnal Ilmu Komputer dan Informasi

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In interactive image segmentation, distance calculation between regions and sequence of region merging is being an important thing that needs to be considered to obtain accurate segmentation results. Region merging without regard to label in Hierarchical Clustering Analysis causes the possibility of two different labels merged into a cluster and resulting errors in segmentation. This study proposes a new multi-class region merging strategy for interactive image segmentation using the Hierarchical Clustering Analysis. Marking is given to regions that are considered as objects and background, which are then referred as classes. A different label for each class is given to prevent any classes with different label merged into a cluster. Based on experiment, the mean value of ME and RAE for the results of segmentation using the proposed method are 0.035 and 0.083, respectively. Experimental results show that giving the label on each class is effectively used in multi-class region merging.

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