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PP: Time series clustering via community detection in Networks

Improvement can be done in fulture:1. the algorithm of constructing network from distance matrix. 2. evolution of sliding time window3. the later processing or visual analysis of generated graphs. Thinking: 1.What's the ground truth in load profiles?…

PP: Learning representations for time series clustering

Problem: time series clustering TSC - unsupervised learning/ category information is not available. time-series clustering for anomaly detection/ pattern detection. Feature-based time series clustering methods typically rely on domain knowledge to ma…

【论文阅读】A practical algorithm for distributed clustering and outlier detection

文章提出了一种分布式聚类的算法,这是第一个有理论保障的考虑离群点的分布式聚类算法(文章里自己说的).与之前的算法对比有以下四个优点: 1.耗时短O(max{k,logn}*n), 2.传递信息规模小:对抗分区O(klogn+t),随机分区O(klogn+t/s) 3.算法有良好的近似保证, 4.能够有效的检测出离群点. 其中,k聚类中心个数,n数据集大小,t离群点个数,s站点数(分区个数) 符号说明: 算法总体描述: 文中提出的算法分为两个阶段,第一阶段的算法是在[1]中改进,将[1]中纯净的数…

论文解读（CGC）《CGC: Contrastive Graph Clustering for Community Detection and Tracking》

论文信息 论文标题:CGC: Contrastive Graph Clustering for Community Detection and Tracking论文作者:Namyong Park, Ryan Rossi, Eunyee Koh, Iftikhar Ahamath Burhanuddin, Sungchul Kim, Fan Du, Nesreen Ahmed, Christos Faloutsos论文来源:2022, AAAI论文地址:download 论文代码:download…

A Node Influence Based Label Propagation Algorithm for Community detection in networks 文章算法实现的疑问

这是我最近看到的一篇论文,思路还是很清晰的,就是改进的LPA算法.改进的地方在两个方面: (1)结合K-shell算法计算量了节点重重要度NI(node importance),标签更新顺序则按照NI由大到小的顺序更新 得到ks值后,载计算一下节点邻居ks值和度值d的比值 (2)当出现次数最多的标签不止一个时,再计算一下标签重要度LI(label importance) 其实就是找到节点相同标签的那些令居计算一个合值,看着也不难啊 (3)最后这个算法使用的是异步传播 下面是我实现的代码 func…

LabelRank非重叠社区发现算法介绍及代码实现（A Stabilized Label Propagation Algorithm for Community Detection in Networks）

最近在研究基于标签传播的社区分类,LabelRank算法基于标签传播和马尔科夫随机游走思路上改装的算法,引用率较高,打算将代码实现,便于加深理解. 这个算法和Label Propagation 算法不同的是计算复杂度较高,对每个标签都确定了概率,但是准确性比Label Propagation算法好. 一.概念 相关概念不再累述,详情见前两篇文章 二.算法思路 首先建立一个标签集合,C={1,2,……n},n是节点的数量.标签概率向量Pi(1*n),Pi(c)=节点i对标签c的概率估计,迭代过程中…

PP: Time series anomaly detection with variational autoencoders

Problem: unsupervised anomaly detection Model: VAE-reEncoder VAE with two encoders and one decoder. They use bidirectional bow-tie LSTM for each part. Why use bow-tie model: to remove noise to some extent when encoding.…

[Localization] R-CNN series for Localization and Detection

CS231n Winter 2016: Lecture 8 : Localization and Detection CS231n Winter 2017: Lecture 11: Detection and Segmentation https://zhuanlan.zhihu.com/qianxiaosi 本篇整理得比较杂,毕竟这一块小知识点较多,故,这里只是笔记收集,暂且不能称之为笔记整理. 以下三篇博文读来甚好,推荐: [目标检测]RCNN算法详解 [目标检测]Fast RCNN算法详解…

PP: Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

From: Stanford University; Jure Leskovec, citation 6w+; Problem: subsequence clustering. Challenging: discover patterns is challenging because it requires simultaneous segmentation and clustering of the time series + interpreting the cluster results…

关于目标检测(Object Detection)的文献整理

本文对CV中目标检测子方向的研究,整理了如下的相关笔记(持续更新中): 1. Cascade R-CNN: Delving into High Quality Object Detection 年份:2018:关键词:Cascade RCNN:引用量:749:推荐指数(1-5):5 描述:一般正常的检测器是用0.5的IOU阈值(用于提出正负样本)训练,但如果提高IOU阈值会降低检测器的表现.这有两个原因: 当训练时,高IOU阈值会减少提出的正样本,引发exponentially vanishin…