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Subspace clustering is projected as a search technique to classify the data or attributes in different clusters, grouping is done by determining the density of data and also identify outliers or data that is not relevant, so that each cluster exists in its own subset. This thesis proposes innovation subspace clustering algorithm based on density connection. In the early stages will be counted dimensional density, density results will be used as input data dimensions to determine the initial clusters based on density-dimensional, ie, by using the DBSCAN algorithm. Data on each cluster will then be tested whether having a relationship with the other data on the cluster, by using Algorithm SUBCLU. Results of this study found bahawa SUBCLU not have an unclustered real dataset, so the perception of the results of the cluster will produce more accurate information for job satisfaction dataset whereas DBSCAN takes more time than the method SUBCLU. For larger and more complex the data, the performance SUBCLU looks more efficient than DBSCAN.

SUBSPACE CLUSTERING ANALYSIS USING DBSCAN AND SUBCLU FOR PROJECTING STMIK JAKARTA ALUMNI JOB