Clustering analysis is one of the critical tasks in machine learning. Traditionally, clustering has been an independent task, separate from outlier detection. Due to the fact that the performance of clustering can be significantly eroded by outliers, a small number of algorithms try to incorporate outlier detection in the process of clustering. However, most of those algorithms are based on unsupervised partition-based algorithms such as k-means. Given the nature of those algorithms, they often fail to deal with clusters of complex, non-convex shapes. To tackle this challenge, we have proposed SSDBCODI, a semi-supervised density-based algorithm. SSDBCODI combines the advantage of density-based algorithms, which are capable of dealing with clusters of complex shapes, with the semi-supervised element, which offers flexibility to adjust the clustering results based on a few user labels. We also merge an outlier detection component with the clustering process. Potential outliers are detected based on three scores generated during the process: (1) reachability-score, which measures how density-reachable a point is to a labeled normal object, (2) local-density-score, which measures the neighboring density of data objects, and (3) similarity-score, which measures the closeness of a point to its nearest labeled outliers. Then in the following step, instance weights are generated for each data instance based on those three scores before being used to train a classifier for further clustering and outlier detection. To enhance the understanding of the proposed algorithm, for our evaluation, we have run our proposed algorithm against some of the state-of-art approaches on multiple datasets and separately listed the results of outlier detection apart from clustering. Our results indicate that our algorithm can achieve superior results with a small percentage of labels.
翻译:集群分析是机器学习的关键任务之一。 传统上, 集群是一个独立的任务, 与外观检测分开。 由于集群的性能会被外星体大大侵蚀, 少数算法试图在组合过程中纳入外星体的探测。 但是, 大多数这些算法都是基于未经监督的分区算法, 如 k- 运算。 鉴于这些算法的性质, 它们往往无法处理复杂、 非 convex 形状的组合。 为了应对这一挑战, 我们提议了SSDBCODI, 这是一种半监督的基于密度的算法。 SSDBCODI将基于密度的算法的优势结合在一起, 这些算法能够处理复杂形状的群体群体, 与半监督的元素。 然而, 这些算法提供了根据几个用户标签调整组合结果的灵活性。 我们还将一个外星体的检测组件与聚合过程。 为了应对这个挑战, 我们提出的外星体的外星体, 用来测量一个半比值, 一个半精度点是如何超越一个半精度的量级值, 用来测量一个直径直径的直径点, 。