Abstract: In data mining, sequential pattern mining is a fundamental and essential field since of its broad possibility of applications spanning from forecasting the user shopping patterns and scientific discoveries. In many applications, the most vital part of mining problem is mining periodic patterns in time series databases. It can be visualized as a tool for forecasting and identifying the potential activities of time-series data. Previous studies have measured hash based asynchronous periodic patterns where uneven occurrences of datasets are sequenced. Still, asynchronous periodic pattern mining has expected less attention and in effective. Most researches are explained about an occurrence of long and closed sequence in an asynchronous pattern by a form of numerical derivatives. To improve the asynchronous pattern mining more effective in this study, researchers are going to present an Ant Colony Optimization technique to identify the pattern matching in a mixed sequences (long and closed sequences) raised in the asynchronous pattern mining. The Ant Colony Optimization algorithm (ACO) is an optimized technique for resolving computational problems which can be condensed to discovering good paths and identified the best pattern from the given database. Using ACO, the optimal pattern matching is made efficiently for mixed sequences (closed or long set of sequences) in the given database. Experimental evaluation will conduct with time series dense data sets in arff format in Weka tool and show the performance improvement of asynchronous pattern mining for mining mixed sequences in the given database in terms of running time, pattern matching factor, optimality rate and scalability.