These factors must be grouped based on acquiring a type of diabetes or not. The acquired factor k. partitions the data into classes with high intra-class similarity or low inter-class similarity. An algorithm starts with a random solution, and iteratively makes small changes to the solution, ...
is still based on the distance based clustering algorithm,such as K-means algorithm.So the author of this paper after consulting some about distance clustering algorithm proposes the rough set decision system in the K-means algorithm for the first application,which is the innovation of this paper...
andthereforecan爷taccuratelyreflectteachers爷comprehensivesituationUsingK-meanstoclustertheevaluationresultsofteachers爷teaching,scientificresearch,andteachers爷codeofmoralityinsomemajorforawholeyear,andanalyzetheclusteringresultsindetailTheresultsshowthattheclusteringalgorithmcanobtainmoreeffectiveinformationfromtheevaluationda...
In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups base
A modified Artificial Hummingbird Algorithm (mAHA) is presented here as an innovative and improved optimizer. In mAHA, local escape operator (LEO) and opposition-based learning (OBL) are integrated into the basic Artificial Hummingbird Algorithm (AHA). An improved version of AHA, mAHA, seeks to...
The schematic of the intrusion detection system is shown in Figure 1. Figure 1 Open in figure viewerPowerPoint Intrusion detection detecting system. 3.1. K-Modes Clustering Algorithm The K-means algorithm is a clustering algorithm commonly used in the field of data mining. However, this algorithm...
The algorithm is similar to the k-means clustering method and is designed to find a dictionary \(\boldsymbol {\Phi } \in \mathbb {R}^{n\times k}\) containing k elements that sparsely represent each of the training samples in Un×L = [u1…ui…uL]. To achieve this goal, the ...
The heavy burden of repeated use of K-means algorithm on many subsamples is unavoidable if a gene clustering algorithm is to live up to its challenge. Thus, a novel algorithm is required to adapt tight clustering algorithm for large gene expression data sets or other large data sets. Our ...
Our method consists of three parts: feature selection, cost-sensitive learning and the proposed ensemble algorithm. The three parts will be introduced in the following sections. The data flow diagram of the proposed method is shown in Fig. 1. The data-level method based on feature selection is...
(2011b). As such, in order to validate the results of the clustering, we rather rely on a k-means algorithm using Dynamic Time Warping (DTW, Sakoe & Chiba, 1978) as a distance between trajectories considering them as sequences of 2D velocity vectors (using DTW Barycenter Averaging method...