| Abstract: | Outlier detection is an imperative field of data mining that has several applications in the field of medical research. Mining outliers based on the notion of rare patterns can be a promising solution for medical diagnosis as it attempts to identify the unconventional and abnormal risk patterns present in medical data. A crucial issue in medical data analysis is the continuous growth of medical databases due to the addition of new records. Existing outlier detection techniques are capable of handling only static data and thus re-execute from scratch to identify the outliers from incremental medical data. This paper introduces an efficient rare pattern based outlier detection (RPOD) method that identifies outliers by mining rare patterns from incremental data. To avoid multiple database scans and expensive candidate generation steps performed by existent rare pattern mining techniques and facilitate incremental mining, a single pass prefix tree-based rare pattern mining technique is proposed. The proposed rare pattern mining technique is a modification of the well-known FP-Growth frequent pattern mining algorithm. Furthermore, to identify the outliers based on the set of generated rare patterns, an outlier detection technique is also presented. The significance of proposed RPOD approach is demonstrated using several well-known medical datasets. Comparative performance evaluation substantiates the predominance of RPOD approach over existing outlier mining methods. |
