The training algorithm of Wavelet Neural Networks (WNN) is a bottleneck which impacts on the accuracy of the final WNN model. Several methods have been proposed for training the WNNs. From the perspective of our research, most of these algorithms are iterative and need to adjust all the parameters of WNN. This paper proposes a one-step learning method which changes the weights between hidden la...

Sequential pattern mining is an extension of association rule mining that discovers time-related behaviors in sequence database. It extends association by adding time to the transactions. The problem of finding association rules concern with intratransaction patterns whereas that of sequential pattern mining concerns with inter-transaction patterns. Generalized Sequential Pattern (GSP) mining a...

Process mining aims at automatically generating process models from event logs. The main idea is to use the discovered models as an objective start point to deploy systems that support the execution of business processes (for instance, workflow management systems) or as a feedback mechanism to check if the prescribed models fit the executed ones. When developing an algorithm to do process minin...

Mining association rules in the database is one of important study in data mining research. Traditional association rules consist of some redundant information, and need scan database many times and generate lots of candidate item sets. Aiming at low efficiency in association rules mining using traditional methods, this paper proposes the algorithm (ISMFP), which is based on intersection for mi...

The huge amount of unstructured data available on the Web and the intranets creates today an information overloading problem. So, managing the knowledge contained in the textual documents is an important problem of Knowledge Management. Knowledge Extraction from collections of data is possible by Knowledge Discovery in Database (KDD), an interactive and iterative process focused on the explorat...

Empirical Bayes methods are privileged in data mining because they can absorb prior information on model parameters and are free of choosing tuning parameters. We proposed an iterated conditional modes/medians (ICM/M) algorithm to implement empirical Bayes selection of massive variables while incorporating sparsity or more complicated a priori information. The algorithm is constructed on the ba...

Data mining has recently attracted attention as a set of efficient techniques that can discover patterns from huge data. More recent advancements in collecting massive evolving data streams created a crucial need for dynamic data mining. In this paper, we present a genetic algorithm based on a new representation mechanism, that allows several phenotypes to be simultaneously expressed to differe...

Data mining is an iterative and interactive process concerned with discovering patterns, associations and periodicity in real world data. This chapter presents two real world applications where evolutionary computation has been used to solve network management problems. First, we investigate the suitability of linear genetic programming (LGP) technique to model fast and efficient intrusion dete...

This paper presents a novel data mining technique, known as Post Sequential Patterns Mining. The technique can be used to discover structural patterns that are composed of sequential patterns, branch patterns or iterative patterns. The concurrent branch pattern is one of the main forms of structural patterns and plays an important role in event-based data modelling. To discover concurrent branc...

Large-scale network and graph analysis has received considerable attention recently. Graph mining techniques often involve an iterative algorithm, which can be implemented in a variety of ways. Using PageRank as a model problem, we look at three algorithm design axes: work activation, data access pattern, and scheduling. We investigate the impact of different algorithm design choices. Using the...