Mining of association rules became one of the strongest elds of data mining This paper presents a classi cation rule learning algo rithm APRIORI C upgrading APRIORI to dealing with classi cation problems decreasing its memory consumption and time complexity fur ther decreasing its time complexity by feature subset selection and im proving the understandability of results by rule post processing...

The gaining returns in line with risks is always a major concern for market play-ers. This study compared the selection of stock portfolios based on the strategy of buying and retaining winning stocks and the purchase strategy based on the level of investment risks. In this study, the two-step optimization algorithms NSGA-II and SPEA-II were used to optimize the stock portfolios. In order to de...

Nowadays, One-Dimensional Cutting Stock Problem (1D-CSP) is used in many industrial processes and re-cently has been considered as one of the most important research topic. In this paper, a metaheuristic algo-rithm based on the Simulated Annealing (SA) method is represented to minimize the trim loss and also to fo-cus the trim loss on the minimum number of large objects. In this method, the 1D-...

In this paper we propose a congestion control mechanism for reliable multicast applications that uses a small set of group members or representatives to provide timely and accurate feedback on behalf of congested subtrees of a multicast distribution tree Our algorithm does not need to compute round trip time RTT from all receivers to the source nor does it require knowledge of group membership ...

We present a randomized DNA algorithm for k SAT based on the classical algorithm of Paturi et al For an n variable m clause instance of k SAT m n our algorithm nds a satisfying assignment assuming one exists with probability e in worst case time O k mn and space O k n log This makes it the most space e cient DNA k SAT algo rithm for k and k n log i e the clause size is small compared to the num...

Object tracking is still a challenging task, especially if it is d o ne in a realistic env iro nm ent. T he o ngo ing increase o f co m pu tatio nal po w er and the efficiency o f the algo rithm s allo w real-tim e estim atio n o f the o bject’s po se in six d egrees o f freed o m . One o f these algo rithm s is the 3 -D hyperplane appro ach, w hich is u sed thro u gho u t this paper, as it has...

T he leading par tit ional clustering technique, K Modes, is one of the most computationally eff icient clustering methods fo r categ orical data. In the t raditional K Modes algo rithm, the simple matching dissim ilarity measure is used to compute the distance betw een two values of the same catego rical at t ributes. T his compares tw o categorical v alues directly and results in either a dif...

A new learning algorithm for feedforward networks, learning by choice of intern al represent at ions (C HIR), was recently introduced [1,2]. W hereas many algor it hm s red uce th e learning proce ss to minimizing a cost function over t he weights, our method treats th e internal representations as the funda ment al ent it ies to be determi ned. T he algo rithm applied a sea rch procedure in th...

In this paper, an iterative method is proposed for solving large general Sylvester matrix equation $AXB+CXD = E$, where $A in R^{ntimes n}$ , $C in R^{ntimes n}$ , $B in R^{stimes s}$ and  $D in R^{stimes s}$ are given matrices and $X in R^{stimes s}$  is the unknown matrix. We present a global conjugate gradient (GL-CG) algo- rithm for solving linear system of equations with multiple right-han...