Mining infrequent patterns in data stream

Abstract

In recent years researches are focused towards mining infrequent patterns rather than frequent patterns. Mining infrequent pattern plays vital role in detecting any abnormal event. In this paper, an algorithm named Infrequent Pattern Miner for Data Streams (IPM-DS) is proposed for mining nonzero infrequent patterns from data streams. The proposed algorithm adopts the FP-growth based approach for generating all infrequent patterns. The proposed algorithm (IPM-DS) is evaluated using health data set collected from wearable physiological sensors that measure vital parameters such as Heart Rate (HR), Breathing Rate (BR), Oxygen Saturation (SPO2) and Blood pressure (BP) and also with two publically available data sets such as e-coli and Wine from UCI repository. The experimental results show that the proposed algorithm generates all possible infrequent patterns in less time.