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The validation of a probabilistic fingerprinting approach for outdoor location estimation using received signal strength (RSS) from GSM base stations (BSs) is described. The proposed approach is compared with a traditional probabilistic algorithm for three different area partitioning methods. Two contrasting real environments are used for the comparisons: one is a city environment and the other one is a rural setting. For each test-bed, over 9000 data points are collected over 170,000 and 110,000 square meters respectively. For each environment, principal components analysis (PCA) is globally used to remove the least useful transmitters to avoid unnecessary calculations. Then each environment is partitioned into different clusters based on RSS. PCA is again used within each cluster. The proposed scheme retains accuracy by not losing the substantial RSS correlations in each cluster, but also accommodates the different RSS distributions in each cluster. The experimental results show that the positioning accuracy is significantly improved and our clustering scheme gives good support for location estimation.