A Novel Technique for Eliminating Parameter Initialization in Clustering

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Menchita Dumlao Byung-Joo Oh


This study revealed the results of simulating the techniques for eliminate cluster initialization in clustering through series of test of different clustering techniques. Clusters of similar database schema from heterogeneous data sources was successfully clustered at an improved accuracy of 93.33%. The accuracy of clusters using self-organizing map neural network (SOM) was improved by implementing the two-step clustering method prior to SOM with an accuracy of 93.33 percent, an increase of 6.33% from the clustering result of SOM. Agglomerative clustering with hierarchical clustering are best combined to come-up with an automatic clustering task, thus, initialization was eliminated.

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Dumlao, M., & Oh, B.-J. (2019). A Novel Technique for Eliminating Parameter Initialization in Clustering. International Journal in Information Technology in Governance, Education and Business, 1(1), 28-38. Retrieved from http://ijitgeb.org/ijitgeb/article/view/18