Lecture

Clusters and Communities

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COM-308: Internet analytics

Internet analytics is the collection, modeling, and analysis of user data in large-scale online services, such as social networking, e-commerce, search, and advertisement. This class explores a number of the key functions of such online services that have ...

Description

This lecture covers the concepts of clustering and community detection. Clustering involves finding sets of points close to each other in a distance metric, while community detection focuses on identifying highly interconnected nodes in a network. The K-means algorithm is introduced for clustering, along with its iterative approximation process. The lecture then transitions to Gaussian Mixture Models (GMM) for clustering, explaining the EM algorithm for GMM. Modularity is discussed as a measure of community strength, with an emphasis on interpreting and calculating modularity. The Louvain method for community detection is presented as a bottom-up approach to building a hierarchy of communities. The lecture concludes by highlighting the challenges of unsupervised techniques and providing references for further study.

Instructor

Matthias Grossglauser

Matthias Grossglauser is a Professor of Computer and Communication Sciences at EPFL in Lausanne, Switzerland, where he co-directs the Information and Network Dynamics lab. His current research interests center on machine learning, stochastic networks, and discrete choice models, and on their applications in artificial intelligence, reinforcement learning, network science, computational social sciences, and recommender systems. He is also a member of the Federal Communications Commission (ComCom), the indepe

Official source

Ontological neighbourhood

Information engineering

Machine learning: Unsupervised learning

Related concepts (32)

Hierarchical clustering

In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories: Agglomerative: This is a "bottom-up" approach: Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. Divisive: This is a "top-down" approach: All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.

Cluster analysis

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.

Single-linkage clustering

In statistics, single-linkage clustering is one of several methods of hierarchical clustering. It is based on grouping clusters in bottom-up fashion (agglomerative clustering), at each step combining two clusters that contain the closest pair of elements not yet belonging to the same cluster as each other. This method tends to produce long thin clusters in which nearby elements of the same cluster have small distances, but elements at opposite ends of a cluster may be much farther from each other than two elements of other clusters.

Unsupervised learning

Unsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.

Clustering high-dimensional data

Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions equals the size of the vocabulary.

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