Mining of Emerging Pattern: Discovering Frequent itemsets in a Stock Data

Data mining is the process of finding valid, useful and understandable pattern in data. Due to the large size of databases, importance of information stored, and valuable information obtained, finding hidden patterns in data has become increasingly significant. Stock data mining plays an important role to visualize the behavior of financial market. Association rule mining algorithms can be used to discover all item associations (or rules) in a dataset that satisfy user-specified constraints, i.e. minimum support and minimum confidence. Since only one minimum support is used for the whole database, it is implicitly assumed that all items are of the same nature and/ or have similar frequencies in the data. Patterns are evaluated by means of generating itemsets using a predefined support and association rules with a higher confidence level. The pattern generated by the frequent itemset of size three is found to be same as being reflected by means of obtained association rules. The pattern so generated helps investors to build their portfolio and use these patterns to learn more about investment planning and financial market