This paper proposes a method for sequential data mining using correlation matrix memory. Here, we use the concept of the Logical Match to mine the indices of the sequential pattern. We demonstrate the uniqueness of the method with both the artificial and the real datum taken from the NCBI databank
Sequential pattern mining finds frequently occurring patterns ordered by time. The problem was first...
International audienceIn this paper we aim at extending the non-derivable condensed representation i...
In this paper we aim at extending the non-derivable condensed representation in frequent itemset min...
This paper addresses the discovery of sequential patterns in very large databases. Most of the exist...
The goal of high-utility sequential pattern mining (HUSPM) is to efficiently discover profitable or ...
Sequential data mining is increasingly important in many domains. WinMiner is a constraint-based alg...
Discovering associations is one of the fundamental tasks of data mining. Its aim is to automatically...
[[abstract]]Sequential pattern mining is a data mining method for obtaining frequent sequential patt...
Sequential Pattern Mining is a widely addressed problem in data mining, with applications such as an...
Sequential pattern mining is a field of data mining with wide applications. Currently, there are a n...
In recent years, mining informative data and discovering hidden information have become increasingly...
In many applications, e.g., bioinformatics, web access traces, system utilisation logs, etc., the da...
We study mining correlations from quantitative databases and show that this is a more effective appr...
This master's thesis is focused on knowledge discovery from databases, especially on methods of mini...
[[abstract]]The task of sequential pattern mining is to discover the complete set of sequential patt...
Sequential pattern mining finds frequently occurring patterns ordered by time. The problem was first...
International audienceIn this paper we aim at extending the non-derivable condensed representation i...
In this paper we aim at extending the non-derivable condensed representation in frequent itemset min...
This paper addresses the discovery of sequential patterns in very large databases. Most of the exist...
The goal of high-utility sequential pattern mining (HUSPM) is to efficiently discover profitable or ...
Sequential data mining is increasingly important in many domains. WinMiner is a constraint-based alg...
Discovering associations is one of the fundamental tasks of data mining. Its aim is to automatically...
[[abstract]]Sequential pattern mining is a data mining method for obtaining frequent sequential patt...
Sequential Pattern Mining is a widely addressed problem in data mining, with applications such as an...
Sequential pattern mining is a field of data mining with wide applications. Currently, there are a n...
In recent years, mining informative data and discovering hidden information have become increasingly...
In many applications, e.g., bioinformatics, web access traces, system utilisation logs, etc., the da...
We study mining correlations from quantitative databases and show that this is a more effective appr...
This master's thesis is focused on knowledge discovery from databases, especially on methods of mini...
[[abstract]]The task of sequential pattern mining is to discover the complete set of sequential patt...
Sequential pattern mining finds frequently occurring patterns ordered by time. The problem was first...
International audienceIn this paper we aim at extending the non-derivable condensed representation i...
In this paper we aim at extending the non-derivable condensed representation in frequent itemset min...