PPT-Weighted Clustering

Margareta Ackerman Work with Shai BenDavid Simina Branzei and David Loker Clustering is one of the most widely used tools for exploratory data analysis Social Sciences Biology. Margareta Ackerman. Work with . Shai. Ben-David, . Simina. . Branzei. , and David . Loker. . Clustering is one of the most widely used tools for exploratory data analysis.. . Social Sciences. Biology. Clustering. (adapted from) Prof. Alexander . Ihler. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. +. Unsupervised learning. Supervised learning. Predict target value (“y”) given features (“x”). Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . David . Harel. and . Yehuda. . Koren. KDD 2001. Introduction. Advances in database technologies resulted in huge amounts of spatial data. The characteristics of spatial data pose several difficulties for clustering algorithms.. 1. A New Criterion for the Fast Detection of Functional Modules in Protein Interaction Networks. Zina. Mohamed Ibrahim. (King’s College, London, UK). Alioune. . Ngom. (University of Windsor, Windsor, Canada). Margareta Ackerman. Work with . Shai. Ben-David, . Simina. . Branzei. , and David . Loker. . Clustering is one of the most widely used tools for exploratory data analysis.. . Social Sciences. Biology. Chen. Reading: [25.1.2, KPM], [Wang et al., 2009], [Yang . & . Chen, 2011] . 2. Outline. Motivation and Background. Internal index. Motivation and general ideas. Variance-based internal indexes. and Cluster Analysis. Dissertation Defense. Nan Li. Committee. : Dr. . Longin. Jan . Latecki. (Advisor). Dr. . Haibin. Ling. Dr. Slobodan . Vucetic. issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values.. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. Tricky because .