From Partition-Based Clustering to Density-Based Clustering: Fast Find Clusters With Diverse Shapes and Densities in Spatial Databases

Jiang, Wang; Zhu, Cheng; Zhou, Yun; Zhu, Xianqiang; Wang, Yilin; Zhang, Weiming

doi:10.1109/access.2017.2780109

Cited by 35 publications

(11 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The way of merging clusters and identification of the node levels can differentiate between agglomerative and divisive hierarchical clustering [58]. Agglomerative hierarchical clustering (AHC) combines the most adjacent pair of clusters, forming a group from bottom to top [59]. Several strategies of AHC are used to identify the distance between clusters, which are single linkage, complete linkage, centroid linkage, average linkage, Ward's method, and the probability-based method [25,58,59].…”

Section: Category 2: Hierarchical Clusteringmentioning

confidence: 99%

“…Agglomerative hierarchical clustering (AHC) combines the most adjacent pair of clusters, forming a group from bottom to top [59]. Several strategies of AHC are used to identify the distance between clusters, which are single linkage, complete linkage, centroid linkage, average linkage, Ward's method, and the probability-based method [25,58,59]. On the other hand, divisive hierarchical clustering is useful to identify clusters with different densities and shapes [58,59].…”

Section: Category 2: Hierarchical Clusteringmentioning

confidence: 99%

“…Several strategies of AHC are used to identify the distance between clusters, which are single linkage, complete linkage, centroid linkage, average linkage, Ward's method, and the probability-based method [25,58,59]. On the other hand, divisive hierarchical clustering is useful to identify clusters with different densities and shapes [58,59]. The method starts from all samples in a group and then splits the samples into two sub-clusters, which are then divided into further sub-clusters and so on [58].…”

Section: Category 2: Hierarchical Clusteringmentioning

confidence: 99%

“…The design of grid-based clustering divides the entire data space into multiple, non-overlapping grid structures [24,59]. This method performs faster than density-based clustering.…”

Section: Category 3: Grid-based Clusteringmentioning

confidence: 99%

“…Usually, the regions contain points with high density in the data space, which makes density-based clustering mistake them as clusters [59]. Mechanisms of aggregation in density can characterize the clustering [45].…”

Section: Category 4: Density-based Clusteringmentioning

confidence: 99%

See 4 more Smart Citations

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

View full text Add to dashboard Cite

Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

show abstract

Section: Category 2: Hierarchical Clusteringmentioning

confidence: 99%

Section: Category 2: Hierarchical Clusteringmentioning

confidence: 99%

Section: Category 2: Hierarchical Clusteringmentioning

confidence: 99%

“…The design of grid-based clustering divides the entire data space into multiple, non-overlapping grid structures [24,59]. This method performs faster than density-based clustering.…”

Section: Category 3: Grid-based Clusteringmentioning

confidence: 99%

Section: Category 4: Density-based Clusteringmentioning

confidence: 99%

See 3 more Smart Citations