Web mining is a computation intensive task even after the mining tool itself has been developed. However, most mining software is developed ad-hoc and usually is not scalable nor reused for other mining tasks. This paper presents a Web mining model and implementation, referred to as WIM – Web Information Mining –, where rapid prototyping is possible. The underlying conceptual model of WIM provi...

Process mining has been gaining significant attention in academia and practice. A promising first step to apply process mining in the audit domain was taken with the mining of process instances from accounting data. However, the resulting process instances constitute graphs. Commonly, timestamp oriented event log formats require a sequential list of activities and do not support graph structure...

Process mining is gaining more and more attention both in industry and practice. As such, the number of process mining products is steadily increasing. However, none of these products allow for composing and executing analysis workflows consisting of multiple process mining algorithms. As a result, the analyst needs to perform repetitive process mining tasks manually and scientific process expe...

Incremental workflow mining is a technique for automatically deriving a process model from the on-going executions of a process. This way, the process model becomes more and more accurate, and is automatically adapted when the process is being changed. Therefore, incremental workflow mining could help in flexible workflow support: In this paper, we describe a setting that combines incremental w...

Privacy concerns over the proliferation of gathering of personal information by various institutions over the internet led to the development of data mining algorithms that preserve the privacy of those whose personal data are collected and analyzed. A novel approach to such privacy preserving data mining algorithms was proposed where the individual datum in a data set is perturbed by adding a ...

Cluster analysis or clustering is the task of assigning a set of objects into groups called clusters. Main task of clustering are explorative data mining, and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. Cluster analysis itself is not one specific algorithm, but t...

This study deals with performance-based design optimization (PBDO) of steel moment frames employing four different metaheuristics consisting of genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), and particle swarm optimization (PSO). In order to evaluate the seismic capacity of the structures, nonlinear pushover analysis is conducted (PBDO). This method is an iterative ...

Clustering divides data objects into groups to minimize the variation within each group. This technique is widely used in data mining and other areas of computer science. K-means is a partitional clustering algorithm that produces a fixed number of clusters through an iterative process. The relative simplicity and obvious data parallelism of the K-means algorithm make it an excellent candidate ...

Negative and positive association rule mining is extract needful information for large database. The generation of negative and positive rule based on interesting pattern and noninteresting pattern of database. The violation of given threshold value such as minimum support and minimum confidence generate some negative rules. The generation of association rule mining dependent some algorithm suc...

Relational clustering with heterogeneous data objects has impact in various important applications, such as web mining, text mining and bioinformatics etc. In this paper, we build a star-structured general model for relational clustering. It is formulated as an orthogonal tri-nonnegative matrix factorization. The model performs matrix approximation among all different data types to look for hid...