Data mining research - PowerPoint PPT Presentation

The Department of Computer Science at Columbia University

The Department of Computer Science at Columbia University. Henning Schulzrinne, Chair Dept. of Computer Science Columbia University 2006. Columbia Computer Science in Numbers. ~34 full-time faculty and lecturers + visitors, postdocs, adjunct faculty, joint appointments (EE, IEOR), …

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Beyond Data Mining: Delivering the Next Generation of Services from Library Data

Beyond Data Mining: Delivering the Next Generation of Services from Library Data. Lynn Silipigni Connaway, Ph.D. Senior Research Scientist OCLC Timothy J. Dickey, Ph.D. Post-Doctoral Researcher OCLC. WorldCat as an “Aggregate Collection”. Data Mining and Analysis of WorldCat:

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Database Research: The Past, The Present, and The Future

Database Research: The Past, The Present, and The Future. Yi-Shin Chen Department of Computer Science National Tsing Hua University yishin@cs.nthu.edu.tw http://www.cs.nthu.edu.tw/~yishin/. Outline. Motivation The Past Evolution of Data Management [Gray 1996]

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Special Topics in Database Systems

Special Topics in Database Systems. Martin Ester Simon Fraser University School of Computing Science CMPT 884 Spring 2009. Introduction. [Fayyad, Piatetsky-Shapiro &amp; Smyth 96].

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Data Mining Research

Data Mining Research. David L. Olson University of Nebraska. Data Mining Research. Business Applications Credit scoring Customer classification Fraud detection Human resource management Algorithms Database related Data warehouse products claim internal data mining Text mining

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Improved Clustering Technique in Marketing Sector

Cluster analysis divides data into meaningful or useful groups (clusters). One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behaviour for investment decisions. Clustering techniques that are being used in Data Mining is presented. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique computational requirements on relevant clustering algorithms with k-means method is one of the clustering techniques. Data mining facilitates marketing sector by classifying customer demographic that can be used to predict which customer will respond to a mailing or buy a particular product and it is very much helpful in growth of business. K means method proposed that will improved in marketing sector and also discuss how to support clustering technique in marketing sector. Experimental results show difference between clustered and non clustered data of marketing product that represent in graphically and theoretically. These results help customer to choose the products and also it saves the time. Pallavi R. Wankhade | Prof. Rajeshri R. Shelke"Improved Clustering Technique in Marketing Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd98.pdf http://www.ijtsrd.com/engineering/computer-engineering/98/improved-clustering-technique-in-marketing-sector/pallavi-r-wankhade

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