不同距离度量下k -均值聚类算法的性能评价

Shruti Kapil, Meenu Chawla
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引用次数: 19

摘要

数据挖掘是一种技术,用于可视化和仔细检查数据,并从数据中获取一些有用的信息,以便这些信息可以用于执行任何有用的工作。因此,聚类是一种被提议用于数据挖掘领域的技术。聚类背后的概念是根据一些习惯特征将对象分配给集群,例如属于一个集群的对象与属于其他集群的对象相似。聚类算法有很多,但k均值聚类被广泛用于庞大数据集的聚类。k-means聚类的占用因子是它的可扩展性、效率和简单性。本文旨在研究k-means聚类和k-means聚类中使用的各种距离函数,如Euclidean距离函数和Manhattan距离函数。实验和结果显示了这些距离函数对k-means聚类的影响。距离函数使用迭代次数,在和平方误差和构建完整模型所花费的时间内进行比较。
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Performance evaluation of K-means clustering algorithm with various distance metrics
Data Mining is the technique used to visualize and scrutinize the data and drive some useful information from that data so that information can be used to perform any useful work. So clustering is the one of the technique that has been proposed to be used in the area of data mining The notion behind clustering is to assigning objects to cluster based upon some customary characteristics such that object belonging to one cluster are similar other than those belonging to other clusters. There are numerous clustering algorithms available but K-means clustering is widely used to form clusters of colossal dataset. The footprint factor for k-means clustering is its scalability, efficiency, simplicity. This proposed paper aims to study the k-means clustering and various distance function used in k-means clustering such as Euclidean distance function and Manhattan distance function. Experiment and results are shown to observe the effect of these distance function upon k-means clustering. The distance functions are compared using number of iterations, within sum squared errors and time taken to build the full model.
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