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首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >A Genetic Algorithm Approach for Clustering Large Data Sets
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A Genetic Algorithm Approach for Clustering Large Data Sets

机译：大数据集聚类的遗传算法

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In this paper we present a sampling approach to run the k-means algorithm in large data sets. We propose a genetic algorithm to guide sampling based on evaluating the fitness of each individual of the population through the k-means clustering algorithm. Although we want a partition with the lowest SSE, our algorithm tries to find the sample with the highest SSE. After finding a good sample the remaining points of the entire data set are clustered using the nearest centroid and, after that, the SSE of the final solution is calculated. Our proposal is applied on a set of public domain data sets and the results are compared against two other methods: the k-means running in a uniform random sample of the data set, and the k-means in the complete data set. The results showed that our algorithm has a good trade off between quality and computational cost, especially for large data sets and higher number of clusters.
机译：在本文中，我们提出了一种在大数据集中运行k-means算法的采样方法。我们提出了一种遗传算法，通过基于k均值聚类算法评估每个个体的适应性来指导抽样。尽管我们想要具有最低SSE的分区，但是我们的算法会尝试查找具有最高SSE的样本。找到好样本后，使用最接近的质心对整个数据集的其余点进行聚类，然后，计算最终解决方案的SSE。我们的建议应用于一组公共领域数据集，并将结果与​​其他两种方法进行比较：k均值在数据集的统一随机样本中运行，k均值在完整数据集中。结果表明，我们的算法在质量和计算成本之间取得了很好的折衷，尤其是对于大型数据集和更多的聚类而言。

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