Mining weighted sequential patterns in a sequence database with a time-interval weight

Discovering forward sequences from temporal data

“…Others like FreeSpan [23], PrefixSpan [38], and CloSpan [34], were developed afterwards and successively improved to discover frequent sequence patterns. Thereafter, lots of research including time-interval sequence patterns [6], hybrid temporal patterns [33], cyclic patterns [26], similar patterns [5], and bioinformatics patterns [11] are proposed.…”

Learning task models in ill-defined domain using an hybrid knowledge discovery framework

“…These have been previously applied, for example, to analyze earthquake data [13] and source code in software engineering [15]. While traditional sequential pattern mining (SPM) algorithms have as their only goal to discover sequential patterns that occur frequently in several transactions of a database [10,[24][25][26], other algorithms have proposed numerous extensions to the problem of sequential pattern mining such as mining patterns respecting time-constraints [13,43], mining compact representations of patterns [15][16][17], mining time-interval weighted sequential patterns [43,45] and incremental mining of patterns [24]. For this work, we developed a custom sequential pattern mining algorithm [12] that combines several features from other algorithms such as accepting time-constraints [13], processing databases with dimensional information [14], and mining a compact representation of all patterns [16,17], and that also adds some original features such as accepting symbols with parameter values.…”

An efficient mining algorithm for maximal weighted frequent patterns in transactional databases

“…Data mining is used to find patterns (or itemsets) hidden within data, and associations among the patterns. In particular, frequent pattern mining plays an essential role in many data mining tasks such as mining association rules [1], interesting measures [3,26], correlations [22,34], sequential patterns [8,29,41,42], constraint-based frequent patterns [5,40], graph patterns [35], emerging patterns [11,19,27] and approximate patterns [39]. Mining information and knowledge from very large databases is not easy since it takes a long time to process large datasets and the amount of discovered knowledge, and because the number of patterns can be significant and redundant.…”

“…Using this property, infrequent patterns can be pruned earlier. Frequent pattern mining has been studied in the area of data mining due to its broad applications in mining association rules [1], interesting measures or correlations [3,22,26,34], sequential patterns [8,29,35,41,42], constraint-based frequent patterns [5], graph patterns [35], emerging patterns [11,19,27] and other data mining tasks. These approaches have focused on enhancing the efficiency of algorithms in which techniques for search strategies, data structures, and data formats have been devised.…”