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Chapter VI
Conclusion
6.1 ConclusionConclusion of this project are :
1. K-Means algorithm can classify the articles according to the similarity
degree well.
2. Clusters has been made can ease the readers to find articles with the
biggest similarity degree.
6.2 Further ResearchK-means clustering algorithm is an algorithm that is very good in the
grouping of the article with the greatest degree of similarity. The articles are
grouped by content owned word. Then the validity of the content of the word in
the article would greatly support the accuracy of the program. The addition of the
appropriate base word stemming process can be done on purpose to the removal
of common words.
K-Means algorithm result can be visualized by a chart. The form of chart is
scatter chart. If this algorithm has two centroid or two clusters only, it can be made
as linier regression chart. If the clusters is more than 2, the result can be visualized
throug multidimensional scalling.
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APPROVAL AND RATIFICATION PAGESTATEMENT OF ORIGINALITYFOREWORDABSTRACTChapter IIntroduction1.1 Background1.2 Scope1.3 Objective
Chapter IILiterature Study2.1 Data Mining2.2 Clustering Algorithm2.3 Algoritma K-Means2.4 Example2.5 Data Structure2.5.1 Two Dimensional Array
Chapter IIIPlanning3.1 Research Metodology3.2 Project Management
Chapter IVAnalysis and Design4.1 Analysis4.1.1 Use Case Diagram4.1.2 Flow Chart
4.2 Design4.2.1 Class Diagram
Chapter VImplementation and Testing5.1 Implementation5.2. Testing
Chapter VIConclusion6.1 Conclusion6.2 Further Research
References