000			02051nam a22002417a 4500
003			ft6078
005			20251127120800.0
008			251127b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aQA76.9.A43 C39 2016
082			_a.
100	1		_aCayabyab, Renee Anne S. and Contreras, Justine Mariel L.
245			_a An enhancement dbscan algorithm applied to faculty performance evaluation
264		1	_a. _b. _cc2016
300			_bUndergraduate Thesis: (BSCS major in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2016.
336			_2text _atext _btext
337			_2 unmediated _a unmediated _b unmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: DBSCAN is a fundamental density cluster formation. Its advantage is that it can discover clusters with arbitrary shapes and size. The algorithm typically regards clusters as dense regions of objects in the data space that are separated by regions of low-density objects. The algorithm has two input parameters, radius Eps and MinPts. It only needs to find out all the maximal density connected spaces to cluster the data points in an attribute space. And these density-connected spaces are the clusters. Every object not contained in any cluster is considered noise and can be ignored. Though DBSCAN has a lot of advantages compared to other clustering algorithms, it also has its own drawbacks. First, the algorithm is not entirely deterministic wherein it cannot determine the correct cluster of a border point. Second, DBSCAN is sensitive to the setting of parameters resulting to unfitting clusters. Third, possible merging of two supposedly separate clusters may occur in the existing algorithm, failing to comply with the goal of cluster analysis. The researchers were able to enhance the existing system by solving the problems stated and have met the objectives presented. The enhanced algorithm satisfied the standards of the possible user through the conducted survey.
526			_aF
655			_aacademic writing
942			_2lcc _cARCHIVES
999			_c37228 _d37228