Mining Frequent Item Sets over Data Streams using Éclat Algorithm

Frequent pattern mining is the process of mining data in a set of items or some patterns from a large database. The resulted frequent set data supports the minimum support threshold. A frequent pattern is a pattern that occurs frequently in a dataset. Association rule mining is defined as to find out association rules that satisfy the predefined minimum support and confidence from a given data base. If an item set is said to be frequent, that item set supports the minimum support and confidence. A Frequent item set should appear in all the transaction of that data base. Discovering frequent item sets play a very important role in mining association rules, sequence rules, web log mining and many other interesting patterns among complex data. Data stream is a real time continuous, ordered sequence of items. It is an uninterrupted flow of a long sequence of data. Some real time examples of data stream data are sensor network data, telecommunication data, transactional data and scientific surveillances systems. These data produced trillions of updates every day. So it is very difficult to store the entire data. In that time some mining process is required. Data mining is the non-trivial process of identifying valid, original, potentially useful and ultimately understandable patterns in data. It is an extraction of the hidden predictive information from large data base. There are lots of algorithms used to find out the frequent item set. In that Apriori algorithm is the very first classical algorithm used to find the frequent item set. Apart from Apriori, lots of algorithms generated but they are similar to Apriori. They are based on prune and candidate generation. It takes more memory and time to find out the frequent item set. In this paper, we have studied about how the éclat algorithm is used in data streams to find out the frequent item sets. Éclat algorithm need not required candidate generation.