The k-means clustering is one of the most popular clustering algorithms in data mining. Recently a lot of research has been concentrated on the algorithm when the dataset is divided into multiple parties or when the dataset is too large to be handled by the data owner. In the latter case,...
In this work, a Partition Enhanced Mining Algorithm (PEMA) is presented to address these problems. In PEMA, the Association Rule Mining Coordinating Agent receives a request and decides the appropriate data sites, partitioning strategy and mining agents to use. The mining process is divided into ...
Computationalanalysisisessentialfortransformingthemassesofmicroarraydataintoamechanisticunderstandingofcancer.Herewepresentamethodforfindinggenefunctionalmodulesofcancerfrommicroarraydataandhaveappliedittocoloncancer.First,acoloncancergenenetworkandanormalcolontissuegenenetworkwereconstructedusingcorrelationsbetweenthegenes.Thenthe...
a brand new partition structure named quadtree with nested multitype tree (QTMT) is applied in the latest codec H.266/VVC. The introduction of QTMT brings in superior encoding performance at the cost of great time-consuming. Therefore, a fast intra ...
The first step of an agglomerative algorithm considers ( − 1)/2 possible fusions of observations to find the closest pair. This number grows quadratically with . For divisive hierarchical clustering, the first step would be to find the best split into two nonempty subsets, and if all ...
Hence, they assume that the data can be organized or manipulated in ways that maximize the performance of the partitioning algorithm. To partition an existing billion-node graph stored in a general-purpose graph system, we must take the data out of the system, convert it in- to a partition...
Hard Partition In subject area: Computer Science A 'Hard Partition' refers to a disjoint partition generated by an algorithm where nodes are grouped into non-overlapping modules within a network. AI generated definition based on: Computer Aided Chemical Engineering, 2012 About this pageSet alert ...
[2] Ester, Martin, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.”In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 226–31. KDD’96. Portland, Oregon: ...
In the initKmix algorithm, a k-means-based clustering algorithm is run many times, and in each run, one of the attributes is used to create initial clusters for that run. The clustering results of various runs are combined to produce the initial partition. This initial partition is then ...
In this paper, we propose a novel data-sampling technique, a Multiclass Neighborhood Repartition-based Oversampling (MC-NRO) algorithm. The innovation of this method lies in it considers local data characteristics of each class to constrain the oversampled neighborhood. MC-NRO calculates the ...