# Lecture 25 – Clustering, Part 2

Presented by Isaac Schmidt

Content by Isaac Schmidt

A reminder – the right column of the table below contains *Quick Checks*. These are **not** required but suggested to help you check your understanding.

Video | Quick Check | |
---|---|---|

25.0 Introduction. |
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25.1 Graphs. Vertices and edges. Types of data that are well-represented by graphs. Differences between graphs and tabular data. |
25.1 | |

25.2 The adjacency matrix, a mathematical representation of graphs. Properties of adjacency matrix. |
25.2 | |

25.3 The Laplacian matrix. Eigenvalues and eigenvectors of the Laplacian matrix. |
25.3 | |

25.4 A spectral clustering algorithm. Eigenvectors as features. Example of algorithm on toy graph. |
25.4 | |

25.5 Constructing a graph from point data. The distance matrix and nearest neighbors. Affinity Matrices. |
25.5 | |

25.6 Case study of spectral clustering on college basketball data. |
25.6 |