Search results for "CURE data clustering algorithm"

showing 4 items of 24 documents

A novel heuristic memetic clustering algorithm

2013

In this paper we introduce a novel clustering algorithm based on the Memetic Algorithm meta-heuristic wherein clusters are iteratively evolved using a novel single operator employing a combination of heuristics. Several heuristics are described and employed for the three types of selections used in the operator. The algorithm was exhaustively tested on three benchmark problems and compared to a classical clustering algorithm (k-Medoids) using the same performance metrics. The results show that our clustering algorithm consistently provides better clustering solutions with less computational effort.

ta113Determining the number of clusters in a data setBiclusteringClustering high-dimensional dataDBSCANComputingMethodologies_PATTERNRECOGNITIONTheoretical computer scienceCURE data clustering algorithmCorrelation clusteringCanopy clustering algorithmCluster analysisAlgorithmMathematics2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
researchProduct

Twister Tries

2015

Many commonly used data-mining techniques utilized across research fields perform poorly when used for large data sets. Sequential agglomerative hierarchical non-overlapping clustering is one technique for which the algorithms’ scaling properties prohibit clustering of a large amount of items. Besides the unfavorable time complexity of O(n 2 ), these algorithms have a space complexity of O(n 2 ), which can be reduced to O(n) if the time complexity is allowed to rise to O(n 2 log2 n). In this paper, we propose the use of locality-sensitive hashing combined with a novel data structure called twister tries to provide an approximate clustering for average linkage. Our approach requires only lin…

ta113Hierarchical agglomerative clusteringta112Fuzzy clusteringBrown clusteringComputer scienceSingle-linkage clusteringcomputer.software_genreHierarchical clusteringLocality-sensitive hashingData setCURE data clustering algorithmlocality-sensitive hashingaverage linkageData miningHierarchical clustering of networkslinear complexityCluster analysishierarchical clusteringAlgorithmcomputerTime complexityProceedings of the 2015 ACM SIGMOD International Conference on Management of Data
researchProduct

Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination

2015

Exact methods for Agglomerative Hierarchical Clustering (AHC) with average linkage do not scale well when the number of items to be clustered is large. The best known algorithms are characterized by quadratic complexity. This is a generally accepted fact and cannot be improved without using specifics of certain metric spaces. Twister tries is an algorithm that produces a dendrogram (i.e., Outcome of a hierarchical clustering) which resembles the one produced by AHC, while only needing linear space and time. However, twister tries are sensitive to rare, but still possible, hash evaluations. These might have a disastrous effect on the final outcome. We propose the use of a metaheuristic algor…

ta113Theoretical computer scienceBrown clusteringComputer scienceCorrelation clusteringSingle-linkage clusteringHierarchical clusteringCURE data clustering algorithmhierrchial clusteringCanopy clustering algorithmHierarchical clustering of networksCluster analysisclustering2015 IEEE Symposium Series on Computational Intelligence
researchProduct

Scalable implementation of dependence clustering in Apache Spark

2017

This article proposes a scalable version of the Dependence Clustering algorithm which belongs to the class of spectral clustering methods. The method is implemented in Apache Spark using GraphX API primitives. Moreover, a fast approximate diffusion procedure that enables algorithms of spectral clustering type in Spark environment is introduced. In addition, the proposed algorithm is benchmarked against Spectral clustering. Results of applying the method to real-life data allow concluding that the implementation scales well, yet demonstrating good performance for densely connected graphs. peerReviewed

ta113ta213Apache SparkComputer sciencedatasetsCorrelation clusteringdata miningcomputer.software_genrealgorithmsSpectral clusteringComputational sciencedependence clusteringData stream clusteringCURE data clustering algorithmScalabilitySpark (mathematics)algoritmitCanopy clustering algorithmData miningtiedonlouhintaCluster analysisclustering algorithmscomputerdata processingtietojenkäsittely
researchProduct