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首页> 外文期刊>Pattern Analysis and Applications >KAGO: an approximate adaptive grid-based outlier detection approach using kernel density estimate
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KAGO: an approximate adaptive grid-based outlier detection approach using kernel density estimate

机译：Kago：一种使用内核密度估计的基于近似自适应网格的异常检测方法

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摘要

Outlier detection approaches show their efficacy while extracting unforeseen knowledge in domains such as intrusion detection, e-commerce, and fraudulent transactions. A prominent method like the K-Nearest Neighbor (KNN)-based outlier detection (KNNOD) technique relies on distance measures to extract the anomalies from the dataset. However, KNNOD is ill-equipped to deal with dynamic data environment efficiently due to its quadratic time complexity and sensitivity to changes in the dataset. As a result, any form of redundant computation due to frequent updates may lead to inefficiency while detecting outliers. In order to address these challenges, we propose an approximate adaptive grid-based outlier detection technique by finding point density using kernel density estimate (KAGO) instead of any distance measure. The proposed technique prunes the inlier grids and filters the candidate grids with local outliers upon a new point insertion. The grids containing potential outliers are aggregated to converge on to at most top-N global outliers incrementally. Experimental evaluation showed that KAGO outperformed KNNOD by more than an order of approximate to 3.9 across large relevant datasets at about half the memory consumption.
机译：异常值检测方法显示它们的疗效，同时提取在入侵检测，电子商务和欺诈性交易等领域的无法预见的知识时。像K到最近邻（KNN）基础的异常检测（KNNOD）技术的突出方法依赖于从数据集中提取异常的距离措施。然而，由于其二次时间复杂性和数据集中的变化的敏感性，knnod具有有效地处理动态数据环境。结果，由于频繁更新导致的任何形式的冗余计算可能导致检测异常值的效率低下。为了解决这些挑战，我们通过使用内核密度估计（KAGO）而不是任何距离测量来提出基于点密度的近似自适应网格的异常检测技术。所提出的技术在新点插入时将Inlier网格置于Inlier网格并通过本地异常值过滤候选网格。包含潜在异常值的网格被汇总以逐渐收敛到最多的全局异常异常值。实验评估表明，Kago在大约在内存消耗的大约一半的大型相关数据集中超过3.9的近似近似的knnod。

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