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Aristides Gionis
Researcher at Royal Institute of Technology
Publications - 316
Citations - 21244
Aristides Gionis is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Approximation algorithm & Graph (abstract data type). The author has an hindex of 58, co-authored 292 publications receiving 19300 citations. Previous affiliations of Aristides Gionis include Yahoo! & Aalto University.
Papers
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Proceedings Article
k-means--: A Unified Approach to Clustering and Outlier Detection
Sanjay Chawla,Aristides Gionis +1 more
TL;DR: It is proved that the problem is NP-hard and then a practical polynomial time algorithm is presented, which is guaranteed to converge to a local optimum, and the approach is formalized as a generalization of the k-means problem.
Posted Content
Quantifying Controversy in Social Media
TL;DR: In this article, a graph-based three-stage pipeline is proposed to detect controversy in social media, which involves building a conversation graph about a topic, partitioning the conversation graph to identify potential sides of the controversy, and measuring the amount of controversy from characteristics of the graph.
Book ChapterDOI
The discrete basis problem
TL;DR: This paper describes a matrix decomposition formulation for Boolean data, the Discrete Basis Problem, and gives a simple greedy algorithm for solving it and shows how it can be solved using existing methods.
Scalable Techniques for Clustering the Web.
TL;DR: This paper aims to efficiently cluster similar pages on the web, using the technique of Locality-Sensitive Hashing (LSH), in which web pages are hashed in such a way that similar pages have a much higher probability of collision than dissimilar pages.
Journal ArticleDOI
Quantifying Controversy on Social Media
TL;DR: A systematic methodological study of controversy detection by using the content and the network structure of social media and a new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy and shows that content features are vastly less helpful in this task.