Data mining is very often regarded as an interactive and iterative process. Users interacting with the data mining system specify the class of patterns of their interest by means of data mining queries involving various types of constraints. It is very likely that a user will execute a series of similar queries, before he or she gets satisfying results. Unfortunately, data mining algorithms cur...

Nowadays, in a competitive and dynamic environment of businesses, organizations need to moni-tor, analyze and improve business processes with the use of Business Process Management Systems(BPMSs). Management, prediction and time control of events in BPMS is one of the major chal-lenges of this area of research that has attracted lots of researchers. In this paper, we present a...

This thesis introduces business process deviance mining, which belongs to the group of process mining, and gives an overview on multiple deviance mining approaches. After that we focus on deviance mining using discriminative patterns, which belongs to the group of sequential patterns mining techniques. In this work we propose new discriminative pattern mining algorithm based on the Declare lang...

it is well known that if the coefficient matrix in a linear system is large and sparse or sometimes not readily available, then iterative solvers may become the only choice. the block solvers are an attractive class of iterative solvers for solving linear systems with multiple right-hand sides. in general, the block solvers are more suitable for dense systems with preconditioner. in this paper,...

With the explosion of the Internet and its use, the development of security mechanims are quite important in order to preserve the confidentiality, integrity, and availability of data stored in computers. However, the growth of intrusions can make such mechanisms almost unusable, in the sense that the computation time or space needed in order to mantain them can grow exponentially. This positio...

decision trees as one of the data mining techniques, is used in credit scoring of bank customers. the main problem is the construction of decision trees in that they can classify customers optimally. this paper proposes an appropriate model based on genetic algorithm for credit scoring of banks customers in order to offer credit facilities to each class. genetic algorithm can help in credit sco...

The KDD process is a non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. This process comprises several steps which are invoked and parametrized in an interactive and iterative manner. A uniform framework for different kinds of patterns and operators is needed to support KDD efficiently and in an integrated way. Furthermore, becau...

Clustering is the task of assigning a set of instances into groups in such a way that is dissimilarity of instances within each group is minimized. Clustering is widely used in several areas such as data mining, pattern recognition, machine learning, image processing, computer vision and etc. K-means is a popular clustering algorithm which partitions instances into a fixed number clusters in an...

In this paper a mathematical simulation of a simplified internal combustion engine is presented. To contribute engine kinematics and its geometry, simple relations are derived for constrained motions. The equation of motion for the piston forms a singular boundary value problem. The uniqueness of the solution was studied in the Banach space. For solving governing equations an iterative numerica...

51 Interactive Data Analysis: The Control Project D ata analysis is fundamentally an iterative process in which you issue a query, receive a response, formulate the next query based on the response, and repeat. You usually don't issue a single, perfectly chosen query and get the information you want from a database; indeed, the purpose of data analysis is to extract unknown information, and in ...