#outlier detection

[bytetrending]

10 Useful NumPy One-Liners for Time Series Analysis

Working with time series data often means wrestling with the same patterns over and over: calculating moving averages, detecting spikes, creating features for forecasting models. This article will explore 10 powerful NumPy one-liners that can significantly streamline your time series analysis workflow. NumPy is an indispensable tool for any data scientist working with numerical data, particularly…

10 Useful NumPy One-Liners for Time Series Analysis

[techenthuinsights]

How to Use Anomaly Detection with Machine Learning?

Learn how to use anomaly detection with machine learning to improve product reliability, boost user trust, and catch issues before users see

[recenttrendingtopics]

Is Outlier Detection the Secret to Accurate Data? Hidden outliers can distort insights and mislead decisions. Detect them early for precise, Data Driven Strategies! Read more: https://bit.ly/3Y56u4L

[pulipuli]

探索性分析：分群與異常偵測 / Exploratory Analysis: Clustering and Outlier Detection

看看網頁版全文 ⇨ 探索性分析：分群與異常偵測 / Exploratory Analysis: Clustering and Outlier Detection https://blog.pulipuli.info/2023/08/exploratory-analysis-clustering-and-outlier-detection.html 這是我在2020年「大數據基本演算」系列課程中教授的「探索性分析：分群與異常偵測」課程內容。 以下是投影片跟相關教材的整理，供有需要的朋友參考。 ---- # 投影片 / Slide https://docs.google.com/presentation/d/1zGdXYPFy5UDgYYUiPMTmbiBEtcWKAZxKefYtTQ1RmqE/edit?usp=sharing。 - Google簡報線上檢視 - PDF格式下載 - Power Point (.pptx) 格式備份：Google Drive、SlideShare、GitHub、One Drive、Mega、Box、MediaFire、pCloud、Degoo、4shared # 投影片大綱 / Outline 本次投影片包含了以下主題： 1. 認識Weka 2. 準備Weka 3. 分群 4. 異常偵測 5. 學習單作業的說明 課程目標如下： - 能夠自行安裝Weka與所需環境 - 能夠使用Weka找出隱含在資料中的模式 (類別) - 能夠使用Weka找出資料中特別的案例 # 相關教材 / Materials https://pulipulichen.github.io/Weka-Cluster-Result-Analyzer/。 本次投影片會用到的教材如下： - 安裝自製套件：WekaODF 1.0.5.zip - 學生資料集 說明 - stu-sch-1 - train.ods (線上檢視) - Weka分群結果分析器 (GitHub保存庫) # 學習單 / Learning Sheet。 https://docs.google.com/document/d/14l-hUJbIh-ErIhj7PDWRChg51ajTaafP-Nb4AzvumTU/edit?usp=sharing。 - Google文件線上檢視 - PDF格式下載 - OpenDocument Text (.odt) 格式備份：Google Drive、GitHub、One Drive、Mega、Box、MediaFire、pCloud、Degoo、4shared 學習單會用到的資料集： - 銀行行銷探索性分析資料集 - 教學意見回饋探索性分析資料集 - 收入普查探索性分析資料集 - 線上購物探索性分析資料集 - 鐵達尼號乘客探索性分析資料集 ---- # 小結 / In closing ---- 繼續閱讀 ⇨ 探索性分析：分群與異常偵測 / Exploratory Analysis: Clustering and Outlier Detection https://blog.pulipuli.info/2023/08/exploratory-analysis-clustering-and-outlier-detection.html

看看網頁版全文 ⇨ 探索性分析：分群與異常偵測 / Exploratory Analysis: Clustering and Outlier Detection https://blog.pulipuli.info/2023/08/exploratory-analysis-clustering-and-outlier-detection.html 這是我在2020年「大數據基本演算」系列課程中教授的「探索性分析：分群與異常偵測」課程內容。 以下是投影片跟相關教材的整理，供有需要的朋友參考。 ---- # 投影片 / Slide https://docs.google.com/presentation/d/1zGdXYPFy5UDgYYUiPMTmbiBEtcWKAZxKefYtTQ1RmqE/edit?usp=sharing。 - Google簡報線上檢視 - PDF格式下載 - Power Point (.pptx) 格式備份：Google Drive、SlideShare、GitHub、One Drive、Mega、Box、MediaFire、pCloud、Degoo、4shared # 投影片大綱 / Outline 本次投影片包含了以下主題： 1. 認識Weka 2. 準備Weka 3. 分群 4. 異常偵測 5. 學習單作業的說明 課程目標如下： - 能夠自行安裝Weka與所需環境 - 能夠使用Weka找出隱含在資料中的模式 (類別) - 能夠使用Weka找出資料中特別的案例 # 相關教材 / Materials https://pulipulichen.github.io/Weka-Cluster-Result-Analyzer/。 本次投影片會用到的教材如下： - 安裝自製套件：WekaODF 1.0.5.zip - 學生資料集 說明 - stu-sch-1 - train.ods (線上檢視) - Weka分群結果分析器 (GitHub保存庫) # 學習單 / Learning Sheet。 https://docs.google.com/document/d/14l-hUJbIh-ErIhj7PDWRChg51ajTaafP-Nb4AzvumTU/edit?usp=sharing。 - Google文件線上檢視 - PDF格式下載 - OpenDocument Text (.odt) 格式備份：Google Drive、GitHub、One Drive、Mega、Box、MediaFire、pCloud、Degoo、4shared 學習單會用到的資料集： - 銀行行銷探索性分析資料集 - 教學意見回饋探索性分析資料集 - 收入普查探索性分析資料集 - 線上購物探索性分析資料集 - 鐵達尼號乘客探索性分析資料集 ---- # 小結 / In closing ---- 繼續閱讀 ⇨ 探索性分析：分群與異常偵測 / Exploratory Analysis: Clustering and Outlier Detection https://blog.pulipuli.info/2023/08/exploratory-analysis-clustering-and-outlier-detection.html

[ijtsrd]

Outlier Detection using Reverse Neares Neighbor for Unsupervised Data

by V. V. R. Manoj | V. Aditya Rama Narayana | A. Bhargavi | A. Lakshmi Prasanna | Md. Aakhila Bhanu" Outlier Detection using Reverse Neares Neighbor for Unsupervised Data" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, 

URL: http://www.ijtsrd.com/papers/ijtsrd11406.pdf  

Direct URL: http://www.ijtsrd.com/computer-science/data-miining/11406/outlier-detection-using-reverse-neares-neighbor-for-unsupervised-data/v-v-r-manoj

ugc listed journals, indexed journal, special issue publication

Data mining has become one of the most popular and new technology that it has gained a lot of attention in the recent times and with the increase in the popularity and the usage there comes a lot of issues/problems with the usage one of it Outlier detection and maintaining the datasets without the expected patterns. To identify the difference between Outlier and normal behavior we use key assumption techniques. We Provide the reverse nearest neighbor technique. There is a connection between the hubs and antihubs, outliers and the present unsupervised detection methods. With the KNN method it will be possible to identify and influence the outlier and antihub methods on real life datasets and synthetic datasets. So, From this we provide the insight of the Reverse neighbor count on unsupervised outlier detection. 

[jbaquerot]

Outlier detection can be a pain point for all data driven companies, especially as data volumes grow. At Netflix we have multiple datasets growing by 10B+ record/day and so there’s a need for automated anomaly detection tools ensuring data quality and identifying suspicious anomalies. Today we are open-sourcing our outlier detection function, called Robust Anomaly Detection (RAD), as part of our Surus project.