Parallel algorithms for hierarchical clustering

Olson, Clark F.

doi:10.1016/0167-8191(95)00017-i

Cited by 334 publications

(171 citation statements)

References 19 publications

(4 reference statements)

Supporting

Mentioning

166

Contrasting

Unclassified

Order By: Relevance

“…One is the hierarchical method, which re-partitions the set until a stopping condition is met, or an aggregation presses which begins by considering each point as a cluster and then merging close clusters until a stopping condition is met [11,15,18,23,24]. The second is the k-means heuristic, where a mean of a cluster is the average of the clusters points.…”

Section: Clusteringmentioning

confidence: 99%

See 1 more Smart Citation

A Maximum Profit Coverage Algorithm with Application to Small Molecules Cluster Identification

Hassin

2006

Experimental Algorithms

View full text Add to dashboard Cite

Section: Clusteringmentioning

confidence: 99%

“…A clustering method called BRITCH is presented in [28]. It uses the hierarchical clustering algorithm from [18]. BRITCH works well for compact well separated clusters, it does not work well when clusters have different sizes or are connected, even by outliers.…”

Section: Clustering With Outliersmentioning

confidence: 99%

A Maximum Profit Coverage Algorithm with Application to Small Molecules Cluster Identification

Hassin

2006

Experimental Algorithms

View full text Add to dashboard Cite

“…These algorithms deal today with a vast amount of input, as introduced for instance by high throughput experiments in biology [10] and by data mining of the world wide web [12]. Throughout the years, extensive research has been done dealing with efficient implementations for such algorithms [19,15,4,9,7,17,6,1]. In this paper we focus on a common class of hierarchical clustering algorithms, which we call Globally Closest Pair (or GCP) clustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

Optimal implementations of UPGMA and other common clustering algorithms

Gronau

Moran

2007

Information Processing Letters

172

115

View full text Add to dashboard Cite

“…The parallelization of MSTs dates as back as far as 1980 [10]. Olson [100] achieved a time complexity of O(V log(V ))) with V / log(V ) processors, with V being the number of vertices of the graph, and Johnson and Metaxas [71] of O(log 3/2 V ) with V + E processors.…”

Section: Parallelizationmentioning

confidence: 99%

Scaling up data mining algorithms: review and taxonomy

García-Pedrajas

Haro-García

2012

Prog Artif Intell

View full text Add to dashboard Cite

The overwhelming amount of data that are now available in any field of research poses new problems for data mining and knowledge discovery methods. Due to this huge amount of data, most of the current data mining algorithms are inapplicable to many real-world problems. Data mining algorithms become ineffective when the problem size becomes very large. In many cases, the demands of the algorithm in terms of the running time are very large, and mining methods cannot be applied when the problem grows. This aspect is closely related to the time complexity of the method. A second problem is linked with performance; although the method might be applicable, the size of the search space prevents an efficient execution, and the resulting solutions are unsatisfactory. Two approaches have been used to deal with this problem: scaling up data mining algorithms and data reduction. However, because data reduction is a data mining task itself, this technique also suffers from scalability problems. Thus, for many problems, especially when dealing with very large datasets, the only way to deal with the aforementioned problems is to scale up the data mining algorithm. Many efforts have been made to obtain methods that can be used to scale up existing data mining algorithms. In this paper, we review the methods that have been used to address the problem of scalability. We focus on general ideas, rather than specific implementations, that can be used to provide a general view of the current approaches for scaling up data mining methods. A taxonomy of the algorithms is proposed, and many examples of different tasks are presented. Among the different techniques used for data mining, we will pay special attention to evolutionary methods, because these methods have been used very successfully in many data mining tasks.

show abstract

Parallel algorithms for hierarchical clustering

Cited by 334 publications

References 19 publications

A Maximum Profit Coverage Algorithm with Application to Small Molecules Cluster Identification

A Maximum Profit Coverage Algorithm with Application to Small Molecules Cluster Identification

Optimal implementations of UPGMA and other common clustering algorithms

Scaling up data mining algorithms: review and taxonomy

Contact Info

Product

Resources

About