Graph preprocessing. Framework for validating data cleaning techniques on binary data.

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Presentation transcript:

Graph preprocessing

Framework for validating data cleaning techniques on binary data

Motivation & Problem Statement

Data cleaning techniques at the data analysis stage

Distance based Outlier Detection * Knorr, Ng,Algorithms for Mining Distance-Based Outliers in Large Datasets, VLDB98 ** S. Ramaswamy, R. Rastogi, S. Kyuseok: Efficient Algorithms for Mining Outliers from Large Data Sets, ACM SIGMOD Conf. On Management of Data, 2000.

Nearest Neighbour Based Techniques

*Knorr, Ng,Algorithms for Mining Distance-Based Outliers in Large Datasets, VLDB98

Distance based approaches

Data cleaning techniques at the data analysis stage

Local Outlier Factor (LOF)* For each data point q compute the distance to the k-th nearest neighbor (k- distance) Compute reachability distance (reach-dist) for each data example q with respect to data example p as: o reach-dist(q, p) = max{k-distance(p), d(q,p)} Compute local reachability density (lrd) of data example q as inverse of the average reachabaility distance based on the MinPts nearest neighbors of data example q o lrd(q) = Compaute LOF(q) as ratio of average local reachability density of q’s k-nearest neighbors and local reachability density of the data record q o LOF(q) = * - Breunig, et al, LOF: Identifying Density-Based Local Outliers, KDD 2000.

Advantages of Density based Techniques Local Outlier Factor (LOF) approach Example: p 2  p 1  In the NN approach, p 2 is not considered as outlier, while the LOF approach find both p 1 and p 2 as outliers NN approach may consider p 3 as outlier, but LOF approach does not  p 3 Distance from p 3 to nearest neighbor Distance from p 2 to nearest neighbor

Local Outlier Factor (LOF)* For each data point q compute the distance to the k-th nearest neighbor (k- distance) Compute reachability distance (reach-dist) for each data example q with respect to data example p as: reach-dist(q, p) = max{k-distance(p), d(q,p)} Compute local reachability density (lrd) of data example q as inverse of the average reachabaility distance based on the MinPts nearest neighbors of data example q lrd(q) = Compaute LOF(q) as ratio of average local reachability density of q’s k-nearest neighbors and local reachability density of the data record q LOF(q) = * - Breunig, et al, LOF: Identifying Density-Based Local Outliers, KDD 2000.