Analysis of Dendrogram Tree for Identifying and Visualizing Trends in Multi-attribute Transactional Data

D.Radha Rani; A.Vini Bharati; P.Lakshmi Durga Madhuri; M.Phaneendra Babu; A.Sravani

doi:https://doi.org/10.14445/22315381/IJETT-V3I1P203

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Volume 3 | Issue 1 | Year 2012 | Article Id. IJETT-V3I1P203 | DOI : https://doi.org/10.14445/22315381/IJETT-V3I1P203

Analysis of Dendrogram Tree for Identifying and Visualizing Trends in Multi-attribute Transactional Data

D.Radha Rani, A.Vini Bharati, P.Lakshmi Durga Madhuri, M.Phaneendra Babu, A.Sravani

Citation :

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), vol. 3, no. 1, pp. 14-18, 2012. Crossref, https://doi.org/10.14445/22315381/IJETT-V3I1P203

Abstract

Most of the data collected by organizations and firms contains multi-attribute and 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.

Keywords

Temporal Data Mining, Clustering, Data mining, data visualization, trend analysis

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