SSM : A Frequent Sequential Data Stream Patterns Miner

Data stream applications like sensor network data, click stream data, have data arriving continuously at high speed rates and require online mining process capable of delivering current and near accurate results on demand without full access to all historical stored data. Frequent sequential mining is the process of discovering frequent sequential patterns in data sequences as found in applications like web log access sequences. Mining frequent sequential patterns on data stream applications contend with many challenges such as limited memory for unlimited data, inability of algorithms to scan infinitely flowing original dataset more than once and to deliver current and accurate result on demand. Existing work on mining frequent patterns on data streams are mostly for non-sequential patterns. This paper proposes SSM-Algorithm (Sequential Stream Mining-algorithm), that uses three types of data structures (D-List, PLWAP tree and FSP-tree) to handle the complexities of mining frequent ...

• Type: Conference
• Year: 2007
• Where: CIDM
• Authors: C. I. Ezeife, Mostafa Monwar
• Keywords: Artificial Intelligence, CIDM 2007, Frequent Sequential Patterns, Mining, Stream