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Frequent Pattern (FP) mining is a significant and well-researched technique of data mining. It is used to extract interesting patterns from large databases by applying association rules, classifier rules, correlation rules, clustering rules, and sequential rules. Many researchers have been highly concentrated on FP mining for the past years. This book reports on novel algorithms, for solving FP mining problems. A comparison of existing algorithms and their hybrid version is also reported for constantly changing databases. In addition, this work analyzes and studies various existing techniques for mining frequent item sets. This work also evaluates the performance of novel algorithms and compared them with classical techniques such as vertical-based layout, horizontal-based layout, and projected database algorithms.