Improved K-means Algorithm Based on optimizing Initial Cluster Centers and Its Application
Data mining is a process of data grouping or partitioning from the large and complex data, and the clustering analysis is an important research field in data mining. The K-means algorithm is considered to be the most important unsupervised machine learning method in clustering, which can divide all the data into k subclasses that are very different from each other. By constantly iterating, the distance between each data object and the center of its subclass is minimized. Because K-means algorithm is simple and efficient, it is applied to data mining, knowledge discovery and other fields. However, the algorithm has its inherent shortcomings, such as the K value in the K-means algorithm needs to be given in advance; clustering results are highly dependent on the selection of initial clustering centers and so on. In order to adapt to the historical data clustering of the geological disaster monitoring system, this paper presents a method to optimize the initial clustering center and the method of isolating points. The experimental results show that the improved k-means algorithm is better than the traditional clustering in terms of accuracy and stability, and the experimental results are closer to the actual data distribution.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringSimilar Papers 제목 키워드 기반
K-means Algorithm Based on Improved Density Peak Algorithm
Abstract：The initial clustering centers and the number Of clusters need to be selected manually in traditional K—means al— gorithm，SO the result of clustering is unstable and easy to fall into local optimal solution．To …
ClusteringImprovement of K Mean Clustering Algorithm Based on Density
The purpose of this paper is to improve the traditional K-means algorithm. In the traditional K mean clustering algorithm, the initial clustering centers are generated randomly in the data set. It is easy to fall into th…
ClusteringCareful Seeding for k-Medois Clustering with Incremental k-Means++ Initialization
K-medoids clustering is a popular variant of k-means clustering and widely used in pattern recognition and machine learning. A main drawback of k-medoids clustering is that an improper initialization can cause it to get …
ClusteringComputational EfficiencyK-Splits: Improved K-Means Clustering Algorithm to Automatically Detect the Number of Clusters
This paper introduces k-splits, an improved hierarchical algorithm based on k-means to cluster data without prior knowledge of the number of clusters. K-splits starts from a small number of clusters and uses the most sig…
ClusteringPositionImproved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this pap…
ClusteringDensity Estimation