文档介绍：摘要

数据挖掘是从大量的、不完全的、有噪音的、随机的数据中获取潜在的、有用的信
息和知识的过程。聚类分析是数据挖掘重要的组成部分，它是一种无监督的学****方法，
不需要关于数据集的先验知识。聚类算法就是根据事物之间的某些属性，把事物聚集成
类，使得不同类中的事物尽可能的相异，而同一类中的事物尽可能的相似。聚类分析已
经被广泛地应用于生活中的各个领域。
K 均值聚类是典型的划分聚类，它因为实现简单，效率高而被广泛的应用，但该算
法存在着需要事先给定簇个数、对初始中心点选择的依赖性和容易陷入局部最优解等问
题。调和 K 均值算法（KHM）虽然有效减小了对初始中心点选择的依赖性，但它仍需要
事先给定簇个数且容易陷入局部最优结果。针对以上问题，本文提出新算法结合蚁群算
法的调和 K 均值算法（ACAKHM），引入了蚁群算法，蚁群算法的特点是可自主聚类，不
需要给定簇个数，且它是全局寻优的启发式随机搜索算法，有较强的鲁棒性，易于与其
他算法相结合。
新算法充分利用了蚁群算法和调和 K 均值聚类算法的优点，先通过蚁群算法对数据
集进行初步聚类，得到簇个数及初始聚类结果，再将蚁群算法得到的初始聚类簇中心点
作为调和 K 均值聚类的初始中心点，选择较优的初始值，以达到获得较优聚类结果的目
的。实验证明新算法有效解决了调和K 均值算法中簇个数需事先给定及聚类算法容易陷
入局部最优的问题。

关键字：数据挖掘；聚类分析；K 均值聚类；调和 K 均值聚类；蚁群算法
Abstract

Data mining is the process that obtaining information and knowledge from a lot of
imperfect noisy random data. Clustering analysis is important part of data mining. It is an
unsupervised learning process and it doesn’t need prior knowledge about data set. Clustering
algorithm is the process that put objects into different clusters according to the attribute of
objects and makes objects in one cluster have higher similarity and objects in different
clusters have slower similarity. Clustering analysis has been used in many field of life.
K-Means cluster is classic partitioning Clustering. It is widely used because it can be
implemented easily and has high efficiency. But the algorithm has some problems. One
problem is that the count of clusters must be decided prior. The other problem is that it
dependent initial cluster centre points and it can reach local minimal result easily. Although
KHM algorithm resolves the problem that dependent on initial cluster centre point it still has
the problem that it need to decide the count of cluster and will reach local minimal value
easily. To resolve these problems we present an algorithm called ACAKHM in this paper. We
add ant colony algorithm to the clustering algorith