Intelligent patterns frequent from large databases refers to the process of discovering meaningful and significant patterns or associations that occur frequently within vast datasets using intelligent data mining techniques. In data mining and pattern recognition, the term "frequent patterns" usually refers to items, sequences, or subsets that appear frequently in a given dataset. These patterns can provide valuable insights into the underlying relationships, trends, and behaviors within the data. Intelligent Patterns: These are meaningful and relevant patterns that are discovered using advanced algorithms and intelligent data analysis techniques. The intelligence here refers to the ability of the algorithms to identify patterns of interest and discard irrelevant or noise patterns. Frequent Patterns: These are patterns that occur frequently or have high support within the dataset. Support refers to the proportion of transactions or instances in which a particular pattern appears. Large Databases: Refers to datasets that are extensive and contain a significant amount of information. Large databases pose challenges for traditional data analysis methods, making intelligent data mining techniques crucial for effective pattern discovery. The process of finding intelligent frequent patterns from large databases typically involves using algorithms like Apriori, FP-Growth, or Eclat, which efficiently search for itemsets or sequences that meet predefined support and confidence thresholds. Applications of discovering frequent patterns include market basket analysis in retail (finding commonly purchased items together), web usage mining (finding frequently visited web pages), bioinformatics (finding frequent gene associations), and more. These patterns are valuable in decision-making, business intelligence, and predictive analytics, as they can reveal hidden relationships and trends within the data that might not be apparent through simple data examination.