Earticle

Outlier detection and clustering technologies are an important branch of data mining, such as combining the two technologies can improve the mining significance. In this paper, both clustering and outlier detection can be the starting point, proposed a DBSCAN-LOF algorithm is the core idea is to use k_ neighbors thought, DBSCAN redefine the core of the object, making the only non-core objects LOF The operation, thereby reducing the original LOF algorithm is computing the number of global objects, and makes no DBSCAN algorithm input parameters Eps. Real and simulated data sets by experimental results confirm that the algorithm to improve the operating efficiency and the LOF algorithm DBSCAN clustering effect, and while producing clustering and outlier detection results.