Online Clustering with Experts
نویسندگان
چکیده
Approximating the k-means clustering objective with an online learning algorithm is an open problem. We introduce a family of online clustering algorithms by extending algorithms for online supervised learning, with access to expert predictors, to the unsupervised learning setting. Instead of computing prediction errors in order to re-weight the experts, the algorithms compute an approximation to the current value of the k-means objective obtained by each expert.
منابع مشابه
BotOnus: an online unsupervised method for Botnet detection
Botnets are recognized as one of the most dangerous threats to the Internet infrastructure. They are used for malicious activities such as launching distributed denial of service attacks, sending spam, and leaking personal information. Existing botnet detection methods produce a number of good ideas, but they are far from complete yet, since most of them cannot detect botnets in an early stage ...
متن کاملOntology Discovery for the Semantic Web Using Hierarchical Clustering
According to a proposal by Tim Berners-Lee, the World Wide Web should be extended to make a Semantic Web where human understandable content is structured in such a way as to make it machine processable. Central to this conception is the establishment of shared ontologies, which specify the fundamental objects and relations important to particular online communities. Normally, such ontologies ar...
متن کاملDesigning and Explaining the Impact Pattern of Online Advertising on Actual Purchasing (Case Study: Atieh Saba Holding)
The purpose of this study is to design and explain the impact pattern of online advertising on actual purchasing (Case Study: Atieh Saba Holding). The study is qualitative based on the objective and data collection process. The population of the study were all marketing and sales experts at Atieh Saba Holding, and among these experts, 10 were selected as the sample. In this study, data were col...
متن کاملIdentifying Indicators Affecting the Evaluation of Service Quality of Medical Centers’ Online Appointment Systems
Introduction: Online queuing systems in medical centers significantly reduce waiting time and costs, and increase patient satisfaction with the quality of services provided. Service provisions with the desired quality through these systems will manage the crowds in the health care centers. In the current situation, gatherings cause an upward trend of the COVID-19 pandemic and subsequent problem...
متن کاملClustering from Multiple Uncertain Experts
Utilizing expert input often improves clustering performance. However in a knowledge discovery problem, ground truth is unknown even to an expert. Thus, instead of one expert, we solicit the opinion from multiple experts. The key question motivating this work is: which experts should be assigned higher weights when there is disagreement on whether to put a pair of samples in the same group? To ...
متن کامل