Search results for "linkage"

showing 3 items of 363 documents

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

Tractional Motion Machines extend GPAC-generable functions

2012

In late 17th century there appeared the Tractional Motion instruments, mechanical devices which plot the curves solving differential equations by the management of the tangent. In early 20th century Vannevar Bush’s Differential Analyzer got the same aim: in this paper we’ll compare the Differential Analyzer mathematical model (the Shannon’s General Purpose Analog Computer, or GPAC) with the Tractional Motion Machine potentials. Even if we will not arrive in defining the class of the functions generated by Tractional Motion Machines, we’ll see how this class will strictly extend the GPAC-generable functions.

tractional motionlinkagesAnalog computationGPACnonholonomic constraintsAnalog computation; tractional motion; GPAC; computable functions; planar mechanisms; linkages; nonholonomic constraintscomputable functionsplanar mechanismsAnalog computation tractional motion GPAC computable functions planar mechanisms linkages nonholonomic constraints
researchProduct