用分治法优化聚类初值，提升大规模分类数据聚类效率与质量。
Fast Clustering of Categorical Big Data
- 通过迭代二分策略生成优质初始聚类中心
- 在大数据集上实现更高聚类质量和更快运行速度
- 适合处理海量分类数据的场景，如用户行为分析
K-Modes算法虽简单，但聚类质量和效率高度依赖初始聚类中心的选择。本文研究了分治型K-Modes（BK-Modes），该方法通过迭代二分一个簇，每次选择距离和最大的簇进行分裂，直至生成K个簇。以数据点到其聚类中心的距离和为分裂选择标准，最终得到的K个簇中心作为K-Modes的初始中心。实验对比了多个初始中心设置的K-Modes及现有最优方法，结果表明，BK-Modes在大规模数据集上表现出优异的聚类质量与效率。
原文摘要 · Abstract (English)
The K-Modes algorithm, developed for clustering categorical data, is of high algorithmic simplicity but suffers from unreliable performances in clustering quality and clustering efficiency, both heavily influenced by the choice of initial cluster centers. In this paper, we investigate Bisecting K-Modes (BK-Modes), a successive bisecting process to find clusters, in examining how good the cluster centers out of the bisecting process will be when used as initial centers for the K-Modes. The BK-Modes works by splitting a dataset into multiple clusters iteratively with one cluster being chosen and bisected into two clusters in each iteration. We use the sum of distances of data to their cluster centers as the selection metric to choose a cluster to be bisected in each iteration. This iterative process stops when K clusters are produced. The centers of these K clusters are then used as the initial cluster centers for the K-Modes. Experimental studies of the BK-Modes were carried out and were compared against the K-Modes with multiple sets of initial cluster centers as well as the best of the existing methods we found so far in our survey. Experimental results indicated good performances of BK-Modes both in the clustering quality and efficiency for large datasets.
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