A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Category 2: Hierarchical Clustering <s> 1. Introduction. 2. Partitioning Around Medoids (Program PAM). 3. Clustering large Applications (Program CLARA). 4. Fuzzy Analysis. 5. Agglomerative Nesting (Program AGNES). 6. Divisive Analysis (Program DIANA). 7. Monothetic Analysis (Program MONA). Appendix 1. Implementation and Structure of the Programs. Appendix 2. Running the Programs. Appendix 3. Adapting the Programs to Your Needs. Appendix 4. The Program CLUSPLOT. References. Author Index. Subject Index. <s> BIB001 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Category 2: Hierarchical Clustering <s> The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using K-means are investigated. The two-stage procedure-first using SOM to produce the prototypes that are then clustered in the second stage-is found to perform well when compared with direct clustering of the data and to reduce the computation time. <s> BIB002 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Category 2: Hierarchical Clustering <s> Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples including gene expression data analysis, genomic sequence analysis, biomedical document mining, and MRI image analysis. However, due to the diversity of cluster analysis, the differing terminologies, goals, and assumptions underlying different clustering algorithms can be daunting. Thus, determining the right match between clustering algorithms and biomedical applications has become particularly important. This paper is presented to provide biomedical researchers with an overview of the status quo of clustering algorithms, to illustrate examples of biomedical applications based on cluster analysis, and to help biomedical researchers select the most suitable clustering algorithms for their own applications. <s> BIB003 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Category 2: Hierarchical Clustering <s> We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally, we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm. This review adds to the earlier version, Murtagh F, Contreras P. Algorithms for hierarchical clustering: an overview, Wiley Interdiscip Rev: Data Mining Knowl Discov 2012, 2, 86–97. WIREs Data Mining Knowl Discov 2017, 7:e1219. doi: 10.1002/widm.1219 ::: ::: This article is categorized under: ::: ::: Algorithmic Development > Hierarchies and Trees ::: Technologies > Classification ::: Technologies > Structure Discovery and Clustering <s> BIB004 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Category 2: Hierarchical Clustering <s> Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22. <s> BIB005 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Category 2: Hierarchical Clustering <s> Gene selection (GS) is an important research area in the analysis of DNA-microarray data, since it involves gene discovery meaningful for a particular target annotation or able to discriminate expression profiles of samples coming from different populations. In this context, a wide number of filter methods have been proposed in the literature to identify subsets of relevant genes in accordance with prefixed targets. Despite the fact that there is a wide number of proposals, the complexity imposed by this problem (GS) remains a challenge. Hence, this paper proposes a novel approach for gene selection by using cluster techniques and filter methods on the found groupings to achieve informative gene subsets. As a result of applying our methodology to Colon cancer data, we have identified the best informative gene subset between several one subsets. According to the above, the reached results have proven the reliability of the approach given in this paper. <s> BIB006 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Category 2: Hierarchical Clustering <s> Spatial data clustering has played an important role in the knowledge discovery in spatial databases. However, due to the increasing volume and diversity of data, conventional spatial clustering methods are inefficient even on moderately large data sets, and usually fail to discover clusters with diverse shapes and densities. To address these challenges, we propose a two-phase clustering method named KMDD (clustering by combining K-means with density and distance-based method) to fast find clusters with diverse shapes and densities in spatial databases. In the first phase, KMDD uses a partition-based algorithm (K-means) to cluster the data set into several relatively small spherical or ball-shaped subclusters. After that, each subcluster is given a local density; to merge subclusters, KMDD utilizes the idea that genuine cluster cores are characterized by a higher density than their neighbor subclusters and by a relatively large distance from subclusters with higher densities. Extensive experiments on both synthetic and real-world data sets demonstrate that the proposed algorithm has a near-linear time complexity with respect to the data set size and dimension, and has the capability to find clusters with diverse shapes and densities. <s> BIB007
Hierarchical clustering's scalability is relatively high in cluster detection BIB005 . One benefit of the method is that it can detect the hierarchical relationship among clusters easily. However, the major drawback associated with hierarchical clustering is the high computational cost. Agglomerative (bottom-up) and divisive (top-down) are the categories of hierarchical clustering BIB003 BIB002  . The way of merging clusters and identification of the node levels can differentiate between agglomerative and divisive hierarchical clustering  . Agglomerative hierarchical clustering (AHC) combines the most adjacent pair of clusters, forming a group from bottom to top BIB007 . 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 BIB004  BIB007 . On the other hand, divisive hierarchical clustering is useful to identify clusters with different densities and shapes  BIB007 . 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  . For AHC, node-level is the diameter of a new cluster formed at the splitting step. The node-level of divisive methods is to divide the groups based on their diameters. Agglomerative nesting (AGNES), EISEN clustering, and divisive analysis (DIANA) are examples of hierarchical clustering BIB006 BIB001 . Garzón and González BIB006 used these clustering techniques to group similar genes before the step of the gene selection.