scholarly journals On Finding All Reducts of Consistent Decision Tables

2015 ◽  
Vol 14 (4) ◽  
pp. 3-10
Author(s):  
Demetrovics Janos ◽  
Vu Duc Thi ◽  
Nguyen Long Giang
Keyword(s):  
Data Processing ◽  
Time Complexity ◽  
Attribute Reduction ◽  
The Other ◽  
Decision Table ◽  
Minimal Subset ◽  
Reduction Problem ◽  
Processing Information ◽  
Decision Tables

Abstract The problem of finding reducts plays an important role in processing information on decision tables. The objective of the attribute reduction problem is to reject a redundant attribute in order to find a core attribute for data processing. The attribute reduction in decision tables is the process of finding a minimal subset of conditional attributes which preserve the classification ability of decision tables. In this paper we present the time complexity of the problem of finding all reducts of a consistent decision table. We prove that this time complexity is exponential with respect to the number of attributes of the decision tables. Our proof is performed in two steps. The first step is to show that there exists an exponential algorithm which finds all reducts. The other step is to prove that the time complexity of the problem of finding all reducts of a decision table is not less than exponential.

scholarly journals A Method for Extracting Knowledge from Decision Tables in Terms of Functional Dependencies

2013 ◽  
Vol 13 (1) ◽  
pp. 73-82 ◽  
Author(s):  
Vu Duc Thi ◽  
Nguyen Long Giang
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Relational Database ◽  
Time Complexity ◽  
Decision Table ◽  
Functional Dependencies ◽  
Worst Case ◽  
Decision Tables

Abstract The problem of extracting knowledge from decision tables in terms of functional dependencies is one of the important problems in knowledge discovery and data mining. Based on some results in relational database, in this paper we propose two algorithms. The first one is to find all reducts of a consistent decision table. The second is to infer functional dependencies from a consistent decision table. The second algorithm is based on the result of the first. We show that the time complexity of the two algorithms proposed is exponential in the number of attributes in the worst case.

scholarly journals Some algorithms related to consistent decision table

2017 ◽  
Vol 33 (2) ◽  
pp. 131-142
Author(s):  
Quang Minh Hoang ◽  
Vu Duc Thi ◽  
Nguyen Ngoc San
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Polynomial Time ◽  
Time Complexity ◽  
Rough Set Theory ◽  
Decision Rules ◽  
The Other ◽  
Decision Table ◽  
Mathematical Tool ◽  
The Given

Rough set theory is useful mathematical tool developed to deal with vagueness and uncertainty. As an important concept of rough set theory, an attribute reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. Rough set theory is also the most popular for generating decision rules from decision table. In this paper, we propose an algorithm finding object reduct of consistent decsion table. On the other hand, we also show an algorithm to find some attribute reducts and the correctness of our algorithms is proof-theoretically. These our algorithms have polynomial time complexity. Our finding object reduct helps other algorithms of finding attribute reducts become more effectively, especially as working with huge consistent decision table.

A NEW TYPE OF ATTRIBUTE REDUCTION FOR INCONSISTENT DECISION TABLES AND ITS COMPUTATION

2010 ◽  
Vol 18 (02) ◽  
pp. 209-222 ◽  
Author(s):  
DONGYI YE ◽  
ZHAOJIONG CHEN
Keyword(s):  
Complexity Analysis ◽  
Attribute Reduction ◽  
Decision Table ◽  
Membership Degree ◽  
Effective Algorithm ◽  
Trade Off ◽  
Reduction Efficiency ◽  
New Type ◽  
Decision Tables

We introduce in this paper a new type of extended attribute reduction called M-reducts for an inconsistent decision table, which is defined to preserve the membership degree to a maximum decision class for each object of the table. It is shown that a M-reduct can actually preserve more decision information than it does by definition, including the maximum decision class itself and all deterministic decision information. Compared with other types of extended attribute reductions, the proposed type of attribute reduction is a better trade-off between the knowledge preserving capability and reduction efficiency. Illustrative examples are given and an effective algorithm for computing a M-reduct based on two summation functions of attribute sets is proposed together with its complexity analysis.

Generating Simply Approximation Spaces by Using Decision Tables

2016 ◽  
Vol 13 (10) ◽  
pp. 7726-7730
Author(s):  
M. El Sayed
Keyword(s):  
Attribute Reduction ◽  
New Method ◽  
Decision Table ◽  
Approximation Space ◽  
Approximation Spaces ◽  
Decision Tables

The proposed from this paper is view the elimination of the attributes (columns) and the duplicate rows and removing superfluous of attributes values. We obtain the incomplete decision table which is different from the decision table and this table contains the necessary values to make decisions, and also in this paper we introduce new method to generate topology from decision table the degree of dependency between condition attributes and the decision attribute, reduction based on simply open sets. And also we introduce new concept namely, minimal simply open sets, and simply open sets. Also we introduce simply approximation space.

scholarly journals Data Analytical Processing in Data Warehouses

2010 ◽  
Vol 2 (1) ◽  
pp. 99-116
Author(s):  
Katarzyna Rostek
Keyword(s):  
Data Processing ◽  
Data Warehouse ◽  
Analytical Data ◽  
Data Warehouses ◽  
Practical Guidelines ◽  
Processing Information ◽  
Scope Of Application ◽  
Analytical Processing ◽  
Effective Use

Data Analytical Processing in Data Warehouses The article presents issues connected with processing information from data warehouses (the analytical enterprise databases) and two basic types of analytical data processing in data warehouse. The genesis, main definitions, scope of application and real examples from business implementations will be described for each type of analysis. There will be presented copyrighted method of knowledge discovering in databases, together with practical guidelines for its proper and effective use in the enterprise.

scholarly journals Optimal scale combination selection for inconsistent multi-scale decision tables

2021 ◽  
Author(s):  
Yingjie Zhu ◽  
Bin Yang
Keyword(s):  
Lattice Model ◽  
Selection Model ◽  
Decision Table ◽  
Scale Selection ◽  
Multi Scale ◽  
Optimal Scale ◽  
Model Based ◽  
Positive Region ◽  
Decision Tables ◽  
Combination Selection

Abstract Hierarchical structured data are very common for data mining and other tasks in real-life world. How to select the optimal scale combination from a multi-scale decision table is critical for subsequent tasks. At present, the models for calculating the optimal scale combination mainly include lattice model, complement model and stepwise optimal scale selection model, which are mainly based on consistent multi-scale decision tables. The optimal scale selection model for inconsistent multi-scale decision tables has not been given. Based on this, firstly, this paper introduces the concept of complement and lattice model proposed by Li and Hu. Secondly, based on the concept of positive region consistency of inconsistent multi-scale decision tables, the paper proposes complement model and lattice model based on positive region consistent and gives the algorithm. Finally, some numerical experiments are employed to verify that the model has the same properties in processing inconsistent multi-scale decision tables as the complement model and lattice model in processing consistent multi-scale decision tables. And for the consistent multi-scale decision table, the same results can be obtained by using the model based on positive region consistent. However, the lattice model based on positive region consistent is more time-consuming and costly. The model proposed in this paper provides a new theoretical method for the optimal scale combination selection of the inconsistent multi-scale decision table.

Sign in / Sign up

Export Citation Format

Share Document