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C-TREND Temporal Cluster Graphs for Identifying and Visualizing Trends in

Abstract:

Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multiattribute (multidimensional) and temporal in nature. Data mining and business intelligence techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. This paper propose a new data analysis and visualization technique for representing trends in multiattribute temporal data using a clustering based approach. This paper introduce Cluster-based Temporal Representation of EvenT Data (C-TREND), a system that implements the temporal cluster graph construct, which maps multiattribute temporal data to a two-dimensional directed graph that identifies trends in dominant data types over time. This paper present temporal clustering-based technique, discuss its algorithmic implementation and performance, demonstrate applications of the technique by analyzing data on wireless networking technologies and baseball batting statistics, and introduce a set of metrics for further analysis of discovered trends.

Existing System:

  •  Existing algorithms uses matrices to produce partition.
  • Distance between the matrices is used for calculation.

Disadvantages:

  • Existing Schemes Consumes more time.

                                   
Proposed System:

  • This project use DENDROGRAM Data structure for storing and Extracting cluster solutions generated by hierarchical clustering algorithms
  • Calculations are made using  Tree structure

Advantages:

  • Efficiency is considerably increased.
  • N is user defined.

System Requirements:

Hardware:

PROCESSOR          :  PENTIUM IV 2.6 GHz
RAM                      :  512 MB DD RAM

Software:

FRONT END                    :  J2EE(JSP)
OPERATING SYSTEM   :  Window’s Xp
                        BACK END                       : Sql Server 2000


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