PDF-A Clustering Method for Information Retrieval

0 Patrice Bellot Marc ElB143ze Laboratoire d. Stat 600. Nonlinear DA. We discussed LDA where our . discriminant. boundary was linear. Now, lets consider scenarios where it could be non-linear. We will discuss:. QDA. RDA. MDA. As before all these methods aim to MINIMIZE the probability of misclassification.. By . Rong. Yan, Alexander G. and . Rong. Jin. Mwangi. S. . Kariuki. 2008-11629. Quiz. What’s Negative Pseudo-Relevance feedback in multimedia retrieval?. Introduction. As a result of high demand of content based access to video information.. INST 734. Module 3. Doug . Oard. Agenda. Ranked retrieval. Similarity-based ranking. Probability-based ranking. Boolean Retrieval. Strong points. Accurate, . if you know the right strategies. Efficient for the computer. CSC . 575. Intelligent Information Retrieval. 2. Source: . Intel. How much information?. Google: . ~100 . PB a . day; 3+ million servers (15 . Exabytes. stored). Wayback Machine has . ~9 . PB + . 100 . Information. Miles Efron, Jana . Diesner. , Peter . Organisciak. , Garrick Sherman, Ana . Lucic. {. mefron. , et al.}@. illinois.edu. GSLIS 2012. TREC: The Text REtrieval Conference. NIST. Web. Legal. 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. What is IR?. Sit down before fact as a little child, . be prepared to give up every conceived notion, . follow humbly wherever and whatever abysses nature leads, . or you will learn nothing. . . -- Thomas Huxley --. Fatemeh. Azimzadeh. Books. (Manning et al., 2008). Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schütze. . Introduction to Information Retrieval. Cambridge University Press, 2008. . 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. 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.. 2. Clustering. Agenda. Clustering Problem and Clustering Applications. Clustering Methodologies and Techniques. Graph-based clustering methods. K-Means and allocation-based methods. Hierarchical Agglomerative Clustering.