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Since the concept of high utility pattern mining was proposed to solve the drawbacks of traditional frequent pattern mining approach that cannot handle various features of real-world applications, many different techniques and algorithms for high utility pattern mining have been developed. Moreover, several advanced methods for incremental data processing have been proposed in recent years as the sizes of recent databases obtained in the real world become larger. In this paper, we introduce the basic concept of incremental high utility pattern mining and analyze various relevant methods. In addition, we also conduct performance evaluation for the methods with famous benchmark datasets in order to determine their detailed characteristics. The evaluation shows that the less candidate patterns make algorithms faster.