Hot-spot and cluster detection is a part of disease surveillance to find out which regions are effected most by the disease. Analysis of these clusters of disease used for longer periods of time can lead to future prediction of disease outbreaks. Dengue is one of the most important arboviral disease in Malaysia which needs proper surveillance and control strategies. Our current study presents hot-spot and cluster detection of dengue outbreaks in the district of Petaling, Selangor in Malaysia during the year 2014 using the registered cases of dengue in the district. Detected hot-spot from this spatio-temporal analysis of registered dengue could provide a trend for the future dengue outbreak predictions. Use of dengue registered cases for the future prediction could be more effective then using land use variables and climate data. R software is used for the hot-spots detection. Custom clustering definition is considered for the analysis. © 2016 Author(s).