WePaMaDM-Outlier Detection：面向海量数据挖掘的基于模式方法的加权异常检测
WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining
AI总结：
该研究提出面向海量数据挖掘领域的WePaMaDM-Outlier Detection加权异常检测方法，验证了异常检测技术的领域依赖性，并探讨了数据建模在监控等领域异常/新颖性检测中的重要性。
摘要：
加权异常检测是一种识别数据集中异常或反常数据点的方法，这些异常可能由人为错误、欺诈或设备故障等多种因素导致。检测异常能够揭示系统故障、欺诈活动及数据模式的关键信息，帮助专家定位这些异常的根本原因。然而，由于输入数据的特性、标注数据的可得性以及问题的特定需求，构建正常数据模式模型以识别异常颇具挑战。本文提出了面向独特海量数据挖掘领域的WePaMaDM-Outlier Detection方法，证明这类技术具有领域依赖性，通常针对特定问题设定开发，不过相似领域可经调整后适配相关方案。本研究还探讨了数据建模在监控、故障检测和趋势分析领域的异常检测技术中的重要性，该任务也被称为新颖性检测，是一种半监督任务，算法在学习正常类别的同时学会识别异常。
英文摘要：
Weighted Outlier Detection is a method for identifying unusual or anomalous data points in a dataset, which can be caused by various factors like human error, fraud, or equipment malfunctions. Detecting outliers can reveal vital information about system faults, fraudulent activities, and patterns in the data, assisting experts in addressing the root causes of these anomalies. However,creating a model of normal data patterns to identify outliers can be challenging due to the nature of input data, labeled data availability, and specific requirements of the problem. This article proposed the WePaMaDM-Outlier Detection with distinct mass data mining domain, demonstrating that such techniques are domain-dependent and usually developed for specific problem formulations. Nevertheless, similar domains can adapt solutions with modifications. This work also investigates the significance of data modeling in outlier detection techniques in surveillance, fault detection, and trend analysis, also referred to as novelty detection, a semisupervised task where the algorithm learns to recognize abnormality while being taught the normal class.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
