C-TREND: Temporal Cluster
Graphs for Identifying and Visualizing Trends in Multiattribute Transactional
Data
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 proposes 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 : Windows
XP
BACK END : Sql Server 2000
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