Bisecting K-Means Clustering Approach for High Dimensional Dataset High dimensional data is phenomenon in real-world data mining applications. Developing effective clustering methods for high dimensional dataset is a chall... R Indhumathi,DS Sathiyabama - 《Data Mining & Knowledge Engineering》 被引量...
For patients who have a cancer, some examples are positive and some are negative. For patients who don't have a cancer, all examples are negative. The dataset has 102K examples. The dataset is biased, 0.6% of the points are positive, the rest are negative. The dataset was made ...
Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely studied problems in this area is the identification of clusters in a multi-dimensional dataset. This paper introduces a sample-based hierarchical adaptive K-means (SHAKM) clustering ...
Kernel Bisecting k-means Clustering for SVM Training Sample Reduction
Spark 1.6 Notebooks (describing the various enhancements for Spark 1.6) dogfood: Various notebooks including AdTech Sample Notebook Quick Start using Python | Scala examples: Example notebooks in various stages of completion including Iris dataset k-means vs. bisecting k-means flights: Various noteboo...
For example, the OSD dataset, with no more than one hundred samples is eclipsed by the TOD dataset, where tens of thousands of samples are present. That fact was taken into consideration in order to obtain an unbiased analytic result. Consequently, the maximum evaluation size was set at 1000...
Improved optimization parameters prediction using the modified mega trend diffusion function for a small dataset problem This paper proposes a modified mega trend diffusion (MTD) function based on the K-means clustering algorithm to generate artificial samples for a training ... N Khamis,H Selamat,FS...
[29, 30], and the human lung cell line dataset from Howitt et al. [30]. On datasets collected from different donors, SNP-based classification was used to obtain the ground-truth labels [11, 12]. For datasets comprising different cell lines, ground-truth labels were obtained by clustering ...
.Themainclustering processconsistsofselectingthefirstunlabelledpointastheclustercentre,thenassigningeachdata pointinthesampledatasettoitsmostsimilarclustercentreaccordingtoboththeuser-definedthreshold andthevalueofsimilarityfunctionineachiteration,andfinallymodifyingtheclustersusingamethod similartok-Means.The...
We applied SampleQC to a large single-nuclei RNA-seq dataset comprising 867k cells over 172 samples (see Availability of data and materials), taken from human brain tissue, including samples from patients with neurodegenerative conditions. This illustrates an intended use case for SampleQC: a data...