Research Article
Random Forest Bagging and X-Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data
Algorithm 1
Random Forest Bagging X-means Clustering.
Input: Number of patterns (i.e. queries).’. | Output: Accurate and timely anti-pattern identification. | 1: Initialize ‘ number of weak X-means clusters’. | 2: Begin. | 3: For each query pattern’. | 4: Randomly assign weight to cluster ‘. | 5: Measure similarity between cluster weight ‘ and input patterns ‘. | 6: If similarity coefficient ‘ is 1, then ‘ and ‘ are more similar. | 7: Else ‘ and ‘ are dissimilar. | 8: End if. | 9: Group similar patterns ‘ into cluster ‘. | 10: If ‘ not grouped into cluster apply Bayesian information criterion to group patterns 11: Else. | 12: Combine all the weak clusters results using (5). | 13: Apply voting using (6). | 14: Return (anti-patterns). | 15: End if. | 16: End for. | 17: End. |
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