1 / 5

CS548 – Project 4

CS548 – Project 4. Skyler Whorton April 12, 2012. Data Processing. Problems & Solutions Outliers e.g. New York City – crimes rates and police force Incomplete data: missing values for LEMAS attributes High dimensionality Solutions Scale attributes to standardize values

sharne
Download Presentation

CS548 – Project 4

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CS548 – Project 4 Skyler Whorton April 12, 2012

  2. Data Processing • Problems & Solutions • Outliers e.g. New York City – crimes rates and police force • Incomplete data: missing values for LEMAS attributes • High dimensionality • Solutions • Scale attributes to standardize values • Fill missing values using attribute’s median value • CFS on two different target features • Five resulting data sets: • Unscaled – Guessed 125 non-conflicting attributes • Scaled – Applied [0, 1] linear transformation • Z-Score-Scaled – Applied Z-score transformation • CFS-NonViolent – NonViolent class, CFS = 9 features • CFS-Violent – Violent target, CFS = 18 features

  3. K-Means Lowest SSE Run CFS Unscaled, Unsupervised SSE: 3062.4 CFS-NonViolent, Unsupervised K=5 SSE: 175.74

  4. Hierarchical Clustering Unscaled - Average CFS-NonViolent - Average

  5. EM Unscaled, LL: -509.19 CFS-NonViolent (sup), LL: -10.667

More Related