K-means Clustering Algorithm and Its Improvement Research
Yanping Zhao, XiaoLai Zhou
Abstract
Abstract Clustering is a typical unsupervised learning method, and it is also very important in natural language processing. K-means is one of the classical algorithms in clustering. In k-means algorithm, the processing mode of abnormal data and the similarity calculation method will affect the clustering division. Aiming at the defect of K-means, this paper proposes a new similarity calculation method, that is, a similarity calculation method based on weighted and Euclidean distance. Experiments show that the new algorithm is superior to k-means algorithm in efficiency, correctness and stability.
Topics & Concepts
Cluster analysisCorrectnessSimilarity (geometry)Computer scienceEuclidean distanceCanopy clustering algorithmAlgorithmCURE data clustering algorithmStability (learning theory)Division (mathematics)Correlation clusteringPattern recognition (psychology)Artificial intelligenceData miningMathematicsMachine learningImage (mathematics)ArithmeticAdvanced Clustering Algorithms ResearchData Mining Algorithms and ApplicationsNetwork Security and Intrusion Detection