Omen: discovering sequential patterns with reliable prediction delays

Constraint Relaxations for Discovering Unknown Sequential Patterns

The main drawbacks of sequential pattern mining have been its lack of focus on user expectations and the high number of discovered patterns. However, the solution commonly accepted – the use of constraints – approximates the mining process to a verification of what are the frequent patterns among the specified ones, instead of the discovery of unknown and unexpected patterns. In this paper, we ...

Discovering and Processing Sequential Patterns in Databases

Discovering Sequential Patterns from Non-Uniform Databases

Databases, when used to collect data in the dynamic world, change over time. As a consequence, di erent parts of a database that come into the database at di erent times may contain activities with di erent characteristics. In other words, the data in the database are \non-uniform". This non-uniformity of the database gives rise to the idea of a Divide and Conquer strategy that divides a very l...

Discovering Novelty in Gene Data: From Sequential Patterns to Visualization

Data mining techniques allow users to discover novelty in huge amounts of data. Frequent pattern methods have proved to be efficient, but the extracted patterns are often too numerous and thus difficult to analyse by end-users. In this paper, we focus on sequential pattern mining and propose a new visualization system, which aims at helping end-users to analyse extracted knowledge and to highli...

Fast Algorithms for Discovering Sequential Patterns in Massive Datasets

Problem statement: Sequential pattern mining is one of the specific data mining tasks, particularly from retail data. The task is to discover all sequential patterns with a user-specified minimum support, where support of a pattern is the number of data-sequences that contain the pattern. Approach: To find a sequence patterns variety of algorithm like AprioriAll and Generalized Sequential Patte...