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Node localization has many applications in wireless networks. For example, it can be used to improve routing and enhance security. Localization algorithms can be classified as range-free or range-based. Range-based algorithms use location metrics such as ToA, TDoA, RSS, and/or AoA to estimate the distance between nodes. Range free algorithms are based on proximity sensing. Range-based algorithms are more accurate but also more computationally complex. However, in applications such as target tracking, localization accuracy is important. In this paper, we propose a new range-based algorithm which is based on decision tree classification and the density based spatial clustering of applications with noise (DB SCAN) algorithm, which are well known in data mining. The Euclidean distance between intersection points is used as a distance metric, and the DBSCAN algorithm is applied to a subset of intersection points based on this metric. Different performance measures are used to compare our localization algorithm with linear least squares (LLS) and weighted linear least squares based on singular value decomposition (WLS SVD). The proposed algorithm is shown to perform better than the LLS and WLS-SVD algorithms even when the anchor geometric distribution about an unlocalized node is poor.