Analysis of Dendrogram Tree for Identifying and Visualizing Trends in Multi - attribute Transactional Data
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2012 by IJETT Journal|
|Year of Publication : 2012|
|Authors : D.Radha Rani, A.Vini Bharati, P.Lakshmi Durga Madhuri, M.Phaneendra Babu, A.Sravani|
D.Radha Rani, A.Vini Bharati, P.Lakshmi Durga Madhuri, M.Phaneendra Babu, A.Sravani. "Analysis of Dendrogram Tree for Identifying and Visualizing Trends in Multi - attribute Transactional Data". International Journal of Engineering Trends and Technology(IJETT). V3(1):14-18 Jan-Feb 2012. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Most of the data collected by organizations and firms contains multi - attribute a nd temporal data. Identifying temporal relationships (e.g., trends) in data constitutes an important problem that is relevant in many business and academic settings. Data mining techniques are used to discover patterns in such data. Temporal data can take many forms, most commonly being general transactional (multi)attribute - value data, for which time series or sequence analysis methods are not particularly well suited. In this paper we present the clustering algorithm with performance and implementation of dataset based on distances in miles between US cities.
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Temporal Data Mining, Clustering, Data mining, data visualization, trend analysis