For set-valued information systems, there are many original dot-based approximation models based on tolerance relations and other developed relations. Because of the lack efficient algorithms, they not to accommodate bigger table. Therefore, it is a real challenge how efficiently calculate high-quality set in systems. To address challenge, we propose reliable operators semi-monolayer covering f...

where A is a mxn matrix of zeroes and ones, e = (1,...,1) is a vector of m ones and c is a vector of n (arbitrary) rational components. This pure 0-1 linear programming problem is called the set covering problem. When the inequalities are replaced by equations the problem is called the set partitioning problem, and when all of the ≥ constraints are replaced by ≤ constraints, the problem is call...

We study a generalization of the Set Cover problem called the Partial Set Cover in the context of geometric set systems. The input to this problem is a set system (X ,S), where X is a set of elements and S is a collection of subsets of X , and an integer k ≤ |X |. The goal is to cover at least k elements of X by using a minimum-weight collection of sets from S. In the geometric version, X typic...

The Set Covering Problem SCP is a main model for several important applications including crew scheduling in railway and mass transit companies In this survey we focus our attention on the most recent and e ective algo rithms for SCP considering both heuristic and exact approaches outlining their main characteristics and presenting an experimental comparison on the test bed instances of Beasley...

This paper proposes a greedy algorithm named as Big step greedy set cover algorithm to compute approximate minimum set cover. The Big step greedy algorithm, in each step selects p sets such that the union of selected p sets contains greatest number of uncovered elements and adds the selected p sets to partial set cover. The process of adding p sets is repeated until all the elements are covered...

The Set Covering Machine (SCM) was introduced by Marchand & Shawe–Taylor [6, 7] in which a minimum set cover of a class of examples was approximated to find a compact conjunction/disjunction of features for classification. Their approach was to solve the set cover problem using the greedy algorithm. In this paper we introduce an alternative method of solving the SCM by formulating it as a Linea...

We define a NP-hard clustered variant of the Set Covering Problem where subsets are partitioned into K clusters and a fixed cost is paid for selecting at least one subset in a given cluster. This variant can reformulate as a master problem various multi-commodity flow problems in transportation planning. We show that the problem is approximable within ratio (1+2)(e/e−1)H(q), where q is the maxi...

The Set Covering Machine (SCM) is a machine learning algorithm that constructs a conjunction of Boolean functions. This algorithm is motivated by the minimization of a theoretical bound. However, finding the optimal conjunction according to this bound is a combinatorial problem. The SCM approximates the solution using a greedy approach. Even though SCM seems very efficient in practice, it is un...

Set multi-covering is a generalization of the set covering problem where each element may need to be covered more than once and thus some subset in the given family of subsets may be picked several times for minimizing the number of sets to satisfy the coverage requirement. In this paper, we propose a family of exact algorithms for the set multi-covering problem based on the inclusionexclusion ...

In this paper, we consider two new variants of the unit covering problem in color-spanning set model: Given a set of n points in d-dimensional plane colored with m colors, the MinCSBC problem is to select m points of different colors minimizing the minimum number of unit balls needed to cover them. Similarly, the MaxCSBC problem is to choose one point of each color to maximize the minimum numbe...