-Vertical partitioning is a design technique for reducing the number of disk accesses to execute a given set of queries by minimizing the number of irrelevant instance variables accessed. This is accomplished by grouping the frequently accessed instance variables as vertical class fragments. The complexity of object-oriented database models due to subclass hierarchy and class composition hierar...

Partitioning a multi-dimensional data set (array) into rectangular regions subject to some constraints (error measures) is an important problem arising from applications in parallel computing, databases, VLSI design, and so on. In this paper, we consider two most common types of partitioning used in practice: the Arbitrary partitioning and (p × p) partitioning, and study their relationships und...

We present filling as a new type of spatial subdivision problem that is related to covering and packing. Filling addresses the optimal placement of overlapping objects lying entirely inside an arbitrary shape so as to cover the most interior volume. In n-dimensional space, if the objects are polydisperse n-balls, we show that solutions correspond to sets of maximal n-balls and the solution spac...

Data mining partitioning is performed on attributes to increase data concentration. To choose effective partitioning attributes, a rough set based technique that measures the crispiness of the partitioning is presented. This technique is based on the mathematical theory of rough sets. This method is a simple and efficient approach to measure the crispiness of the partition in data mining.

Set Covering Problem and Set Partitioning Problem can model several real life situations. In this paper we solve some benchmarks of them with Ant Colony Optimization algorithms and some hybridizations of them with Constraint Programming techniques. The Lookahead mechanism allows the incorporation of information on the anticipated decisions that are beyond the immediate choice horizon. Computati...

In machine learning area, as the number of labeled input samples becomes very large, it is very difficult to build a classification model because of input data set is not fit in a memory in training phase of the algorithm, therefore, it is necessary to utilize data partitioning to handle overall data set. Bagging and boosting based data partitioning methods have been broadly used in data mining...

We study the problem of partitioning a rectangle S with a set of interior points Q into rectangles by introducing a set of line segments of least total length. The set of partitioning line segments must include every point in Q. Since this prob/em is computationally intractable (NP-hard), several approximation algorithms for its solution have been developed. In this paper we show that the lengt...

Any set system can be represented as an n -cube vertices set. Restricted sets of n -cube weighted subsets are considered. The problem considered is in simple description of all set of partitioning characteristic vectors. A smaller generating sets are known as “boundary” and ”steepest” sets and finally we prove that the intersection of these two sets is also generating for the partitioning chara...

A delivery problem which reduces to the NP-complete set-partitioning problem is investigated. The sequential and parallel simulated annealing algorithms to solve the delivery problem are discussed. The objective is to improve the quality of solutions to the problem by applying parallelism.

Evolvable Hardware (EHW) has been proposed as a new method for evolving circuits automatically. One of the problems appearing is that only circuits of limited size are evolvable. In this paper it is shown that by applying an approach where the training set as well as each training vector is partitioned, large combinational circuits can be evolved. By applying the proposed scheme, it is shown th...