ApproxMAP: Approximate Mining of Consensus Sequential Patterns

Abstract

Sequential pattern mining is an important data mining task with broad applications. However, conventional methods may meet inherent difficulties in mining databases with long sequences and noise. They may generate a huge number of short and trivial patterns but fail to find interesting patterns approximately shared by many sequences. To attack these problems, in this paper, we propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. We present an efficient and effective algorithm, ApproxMAP (for APPROXimate Multiple Alignment Pattern mining), to mine consensus patterns from large sequence databases. The method works in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. A novel structure called weighted sequence is used to compress the alignment result. For each cluster, the longest consensus pattern best representing the cluster is generated from its weighted sequence. Our extensive experimental results on both synthetic and real data sets show that ApproxMAP is robust to noise and both effective and efficient in mining approximate sequential patterns from noisy sequence databases with lengthy sequences. In particular, we report a successful case of mining a real data set which triggered important investigations in welfare services.

Computer science and decision theory

“…Much of the data in these databases is in the form of sequences and we often seek sequences that occur frequently or are "centrally located" or form a "consensus pattern." An example arises in social welfare data [245,246], where an algorithm for finding approximate sequential consensus patterns, ones that appear frequently in a database, is discussed. A similar problem arises in molecular biology, when we seek to choose sequences that occur frequently or are "centrally located" in a database of molecular sequences.…”

Mapping frequent spatio-temporal wind profile patterns using multi-dimensional sequential pattern mining

“…This combination may generate large sequences. However, mining sequential patterns is inefficient with long sequences and often may not find exact matching of long patterns in the database (Kum et al 2003). Due to these shortcomings, an improved MDSPM technique is required to mine patterns without loosing important information carried by the dimensions.…”

A Grammatical Inference Sequential Mining Algorithm for Protein Fold Recognition

“…It is one of the essential data mining tasks widely used in many applications, including customer purchase pattern analysis and biological data sequences [17][18][19][20][21][22], etc. Many research have been performed to efficient sequential pattern mining, such as [23][24][25], closed and maximal sequential pattern mining [26][27][28][29], constraint-based sequential pattern mining [30][31][32] approximate sequential pattern mining [33], sequential pattern mining in multiple data sources [34], sequential pattern mining in noisy data [35], incremental mining of sequential patterns [36], and time-interval weighted sequential pattern mining [37]. Two of the general sequential mining algorithms are SPADE [24] and PrefixSpan [23], which are more efficient than others in terms of processing time.…”