A new iterative learning controller is proposed for a general unknown discrete time-varying nonlinear non-affine system represented by NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) model. The proposed controller is composed of an iterative learning neural identifier and an iterative learning controller. Iterative learning control and iterative learning identification ar...

Data mining is an interactive and iterative process. Users issue series of similar queries until they receive satisfying results, yet currently available data mining systems do not support iterative processing of data mining queries and do not allow to re-use the results of previous queries. Consequently, mining algorithms suffer from long processing times, which are unacceptable from the point...

Mining generalized association rules among items in the presence of taxonomy and with nonuniform minimum support has been recognized as an important model in data mining. In our previous work, we have investigated this problem and proposed two algorithms, MMS_Cumulate and MMS_Stratify. In real applications, however, the work of discovering interesting association rules is an iterative process. ...

Data mining is an attempt to automatically extract useful information from passive data using various artificial intelligence techniques [2]. Conventional database systems offer little support for data mining applications. At the same time, statistical and machine learning techniques usually perform poorly when applied to large data sets. These twin limitations suggest the development of algori...

consider the following consistent sylvester tensor equation[mathscr{x}times_1 a +mathscr{x}times_2 b+mathscr{x}times_3 c=mathscr{d},]where the matrices $a,b, c$ and the tensor $mathscr{d}$ are given and $mathscr{x}$ is the unknown tensor. the current paper concerns with examining a simple and neat framework for accelerating the speed of convergence of the gradient-based iterative algorithm and ...

Fig-1 The data stored in the world are rapidly growing. This growth of databases has far outpaced the human ability to interpret this data creating new phenomena of big data. Big data is difficult to work using most existing tools, automatic methods and visualization. Some new methods called Visual data Mining have recently appeared trying to involve more significantly the user in the data mini...

The majority of process mining techniques focuses on control flow. Decision Point Analysis (DPA) exploits additional data attachments within log files to determine attributes decisive for branching of process paths within discovered process models. DPA considers only single attribute values. However, in many applications, the process environment provides additional data in form of consecutive m...

Mining association rules in large database is one of data mining and knowledge discovery research issue, although many algorithms have been designed to efficiently discover the frequent pattern and association rules, Apriori and its variations are still suffer the problem of iterative strategy to discover association rules, that’s required large process. In Apriori and Apriori-like principle it...

In this paper, we introduce and study a mixed variational inclusion problem involving infinite family of fuzzy mappings. An iterative algorithm is constructed for solving a mixed variational inclusion problem involving infinite family of fuzzy mappings and the convergence of iterative sequences generated by the proposed algorithm is proved. Some illustrative examples are also given.

Web usage mining involves application of data mining techniques to discover usage patterns from the web data. Clustering is one of the important functions in web usage mining. Recent attempts have adapted the C-means clustering algorithm as well as genetic algorithms to find sets of clusters .In this paper; we have proposed a new framework to improve the web sessions’ cluster quality from fuzzy...