PPT-Exploiting Big Data via Attributes

Offline Contd Recap Attributes What are attributes Slide Credit Devi Parikh Recap Attributes Rich Understanding Image Credit Ali Farhadi Recap Annotations Zeroshot learning Frogs are green have heads and legs What is. Remark: Discusses “basics concerning data sets (first half of Chapter 2) but does not discuss preprocessing. Preprocessing will be discussed in . late October. . What is Data?. Collection of data objects and their attributes. Data. Data topics. Types of attributes. Data quality issues. Transformations. Visualization. Types of datasets. Preprocessing. Summary statistics. What is data?. Collection of data objects and their attributes. Mining . Methods Course. Dr. Russell Anderson. Dr. Musa Jafar. West Texas A&M University. What is Data Mining?. The process of discovering useful information in large data repositories. . (Tan, P-N., Steinbach, M., and Kumar, V., Introduction to Data Mining, Addison-Wesley, 2006). Disclosure. Vincent. CH14. I. ntroduction. In . this chapter, we . will try to. . extract further information from an application during an . actual attack. . . This mainly involves . I. nteracting . Data Preparation & Preprocessing. Bamshad Mobasher. DePaul University. 2. The Knowledge Discovery Process. - The KDD Process. 3. Data Preprocessing. Why do we need to prepare the data?. In real world applications data can be . Clustering (Chap 7). Introduction. Clustering is an important data mining task.. Clustering makes it possible to “almost automatically” summarize large amounts of high-dimensional data.. Clustering (aka segmentation) is applied in many domains: retail, web, image analysis, bioinformatics, psychology, brain science, . An attribute is a property or. characteristic of an object. Examples: eye color of a. person, temperature, etc.. An Attribute is also known as variable,. field, characteristic, or feature. A collection of attributes describe an object. Identify what caused . the outcomes of . Development Programmes. Using . Eval. C3. Rick Davies. Mark Skipper. Hur Hassnain. Activities &. Assumptions. Planning. Implementation. M&E. Programme. Luke Rasmussen. Feinberg School of Medicine, Northwestern University. luke.rasmussen@northwestern.edu. @. lrasmus. AMIA TBI/CRI 2015 Tutorial. Disclaimers. I receive funding from:. NIH: NHGRI, NIGMS, NCATS. Do . W. e . know?. David Greene, U. Tennessee. Anushah. . Hossain. , Julia Hofmann, Robert Beach, RTI Int.. Gloria . Helfand. , USEPA. Funding for this project was provided by the US EPA.. The content of this presentation does not necessarily reflect the views of the US EPA, the University of Tennessee or RTI International.. Data Preparation & Preprocessing. Bamshad Mobasher. DePaul University. 2. The Knowledge Discovery Process. - The KDD Process. 3. Data Preprocessing. Why do we need to prepare the data?. In real world applications data can be . . . Texas. . Tech. . University. . Suren. . Byna. . . . Lawrence Berkeley National Laboratory. . . Houjun. . Tang. . Introduction to Data Mining. , 2. nd. Edition. by. Tan, Steinbach, Kumar. Outline. Attributes and Objects. Types of Data. Data Quality. Similarity and Distance. Data Preprocessing. What is Data?. Collection of . What Is Data Mining?. Many people treat data mining as a synonym for another popularly used term, knowledge discovery from data, or KDD, while others view data mining as merely an essential step in the process of knowledge discovery. .