Data mining involves extracting Knowledge Discovery in Databases (KDD) is a comprehensive process of extracting useful and previously unknown information from large data sets. It discovers intriguing or valuable patterns and connections within the data.Two types of patterns are discussed: (1) Emerging Patterns and (2) Frequent Patterns. Emerging Patterns are those whose frequency changes significantly between datasets. The Frequent Pattern-Tree method uses a generate-and-test approach, where candidate item sets are generated and then tested for frequency. This paper also covers the FP-Growth algorithm and emphasizes the significance of correlation analysis between patterns. Gene expression data refers to the characteristics of living organisms. Emerging Patterns and Frequent Patterns are applied to gene data to reduce the gene dataset.