DYNAMIC REDUCTS GENERATION USING CASCADING HASHES

2014 ◽  
Vol 25 (02) ◽  
pp. 219-246 ◽  
Author(s):  
PAI-CHOU WANG
Keyword(s):  
Rough Set ◽  
Execution Time ◽  
Random Sampling ◽  
Hash Function ◽  
Time Complexity ◽  
Decision Table ◽  
Hash Tables ◽  
Dynamic Reduct ◽  
Original Classification

Reducts preserve original classification properties using minimal number of attributes in a table. Dynamic reducts are the most stable reducts in the process of random sampling of original decision table, and they are proposed to classify unseen cases. Classical reduct generation methods can be applied to compute dynamic reducts but the time complexity of computing dynamic reducts are rarely discussed. This paper proposes a cascading hash function, and dynamic reduct can be derived in O(m2n) time with O(mn) space where m and n are total number of attributes and total number of instances of the table. Core of dynamic reducts is also discussed, and the computation of core of dynamic reducts takes O(mn) time with O(mn) space. Sixteen UCI datasets are applied to compute (F, ε)-dynamic reducts for ε = 1, and results are compared to Rough Set Exploration System (RSES). Results show the execution time on generating dynamic reducts using cascading hash tables is faster than RSES up to 1700 times. Besides the efficiency issue of the algorithms, our algorithms are also very easy to implement and applicable to any system.

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.

Application of Rough Set Theory to Rule‐type Knowledge Discovery from Field Inspection Data on Highway Bridges

2017 ◽  
Author(s):  
Ayaho Miyamoto
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Highway Bridges ◽  
Decision Table ◽  
Field Inspection ◽  
Rule Type ◽  
Inspection Data ◽  
Contradictory Data

This paper describes an acquisitive method of rule‐type knowledge from the field inspection data on highway bridges. The proposed method is enhanced by introducing an improvement to a traditional data mining technique, i.e. applying the rough set theory to the traditional decision table reduction method. The new rough set theory approach helps in cases of exceptional and contradictory data, which in the traditional decision table reduction method are simply removed from analyses. Instead of automatically removing all apparently contradictory data cases, the proposed method determines whether the data really is contradictory and therefore must be removed or not. The method has been tested with real data on bridge members including girders and filled joints in bridges owned and managed by a highway corporation in Japan. There are, however, numerous inconsistent data in field data. A new method is therefore proposed to solve the problem of data loss. The new method reveals some generally unrecognized decision rules in addition to generally accepted knowledge. Finally, a computer program is developed to perform calculation routines, and some field inspection data on highway bridges is used to show the applicability of the proposed method.

Rough Set Classification Algorithms Based on Texture Feature

2013 ◽  
Vol 347-350 ◽  
pp. 3119-3122
Author(s):  
Yan Xue Dong ◽  
Fu Hai Huang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Texture Classification ◽  
Texture Feature ◽  
Decision Table ◽  
Classification Algorithms ◽  
Reduction Algorithm ◽  
Discrete Method ◽  
Knowledge Reduction

The basic theory of rough set is given and a method for texture classification is proposed. According to the GCLM theory, texture feature is extracted and generate 32 feature vectors to form a decision table, find a minimum set of rules for classification after attribute discretization and knowledge reduction, experimental results show that using rough set theory in texture classification, accompanied by appropriate discrete method and reduction algorithm can get better classification results

scholarly journals ANALISIS PERBANDINGAN ALGORITMA SIMETRIS RIVEST CODE 5 DENGAN ALGORITMA SIMETRIS RIVEST CODE 6) (Studi Kasus : SMK Negeri Seluma)

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Rozali Toyib ◽  
Ardi Wijaya
Keyword(s):  
Execution Time ◽  
Hash Function ◽  
Storage Media ◽  
Encryption And Decryption ◽  
Secure Data ◽  
Computer Based ◽  
Message Digest ◽  
Set Up

Abstack: Data stored in storage media is often lost or opened by certain parties who are not responsible, so that it is very detrimental to the owner of the data, it is necessary to secure data so that the data can be locked so that it cannot be opened by irresponsible parties. The RC5 and RC6 algorithms are digestive massage algorithms or sometimes also known as the hash function which is an algorithm whose input is a message whose length is not certain, and produces an output message digest from its input message with exactly 128 bits in length. RC6 password is a protection for the user in securing data on a PC or computer. Based on the results of the conclusions taken: For the experiments carried out on the RC5 algorithm the execution time for the generation of keys (set-up key) is very fast, which is about 9-10 ns, a trial carried out on the RC6 algorithm execution time for the key generator (set up key ) faster than 10-11 ns. In the encryption and decryption process, the execution time depends on the size or size of the plaintext file. The larger the size of the plaintext file, the longer the execution time.Abstrak : Data yang tersimpan dalam media penyimpanan sering hilang atau dibuka oleh pihak-pihak tertentu yang tidak bertanggung jawab, sehinga merugikan sekali bagi pemilik data tersebut, untuk itu diperlukan suatu pengamanan data agar data tersebut dapat terkunci sehingga tidak dapat dibuka oleh pihak yang tidak bertanggung jawab.. Algoritma RC5 dan RC6 merupakan algoritma massage digest atau kadang juga dikenal dengan hash function yaitu suatu algoritma yang inputnya berupa sebuah pesan yang panjangnya tidak tertentu, dan menghasilkan keluaran sebuah message digest dari pesan inputnya dengan panjang tepat 128 bit. Password RC6 merupakan salah satu perlindungan kepada user dalam pengamanan data yang berada dalam sebuah Pc atau computer. Berdasarkan hasil pengujian diambil kesimpulan : Untuk uji coba yang dilakukan pada algoritma RC5 waktu eksekusi untuk pembangkitan kunci  (set up key) sangat cepat sekali yaitu sekitar  9-10 ns, uji coba yang dilakukan pada algoritma RC6 waktu eksekusi untuk pembangkit kunci (set up key) lebih cepat sekali yaitu 10-11 ns, Pada proses enkripsi dan dekripsi, waktu eksekusi tergantung dari besar atau kecilnya ukuran file plaintext.s emakin besar ukuran file plaintext maka semakin lama waktu eksekusinya.

A Method of Image Affective Semantic Classification Based on Attribution Reduction

2014 ◽  
Vol 556-562 ◽  
pp. 4820-4824
Author(s):  
Ying Xia ◽  
Le Mi ◽  
Hae Young Bae
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Classification Accuracy ◽  
Selection Process ◽  
Attribute Reduction ◽  
Image Features ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Semantic Classification ◽  
Low Level

In study of image affective semantic classification, one problem is the low classification accuracy caused by low-level redundant features. To eliminate the redundancy, a novel image affective classification method based on attributes reduction is proposed. In this method, a decision table is built from the extraction of image features first. And then valid low-level features are determined through the feature selection process using the rough set attribute reduction algorithm. Finally, the semantic recognition is done using SVM. Experiment results show that the proposed method improves the accuracy in image affective semantic classification significantly.

Measures for evaluating the decision performance of a decision table in rough set theory

2008 ◽  
Vol 178 (1) ◽  
pp. 181-202 ◽  
Author(s):  
Yuhua Qian ◽  
Jiye Liang ◽  
Deyu Li ◽  
Haiyun Zhang ◽  
Chuangyin Dang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Table ◽  
Decision Performance

Rules Mining of Power System Transient Stability Assessment Based on Clustering Analysis and Rough Set Theory

2014 ◽  
Vol 521 ◽  
pp. 418-422 ◽  
Author(s):  
Yan Xu ◽  
Xin Chen
Keyword(s):  
Power System ◽  
Set Theory ◽  
Rough Set ◽  
Clustering Analysis ◽  
Transient Stability ◽  
Rough Set Theory ◽  
Decision Table ◽  
Final Decision ◽  
Stability Assessment ◽  
Decision Attributes

Transient Stability Assessment (TSA) aims at assessing stability of power system operation state quickly. This paper introduces rough set theory and clustering analysis to assess power system transient stability. At first, the stability operation parameters and fault places are taken as feature attributes based on the trait of power system transient ability. K-means algorithm is used to make continuous attributes among feature attributes discrete. Then feature attributes and stability types are taken as conditional attributes and decision attributes respectively. Initial decision table is established. Finally, rough set theory is used to form final decision table and rules of TSA are obtained. The IEEE 9-Bus system is employed to demonstrate the validity of the proposed approach.

Fuzzy Attribute Reduction Based on Fuzzy Similarity

2014 ◽  
Vol 533 ◽  
pp. 237-241
Author(s):  
Xiao Jing Liu ◽  
Wei Feng Du ◽  
Xiao Min
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Equivalence Relation ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Fuzzy Decision ◽  
Core Content ◽  
Fuzzy Similarity

The measure of the significance of the attribute and attribute reduction is one of the core content of rough set theory. The classical rough set model based on equivalence relation, suitable for dealing with discrete-valued attributes. Fuzzy-rough set theory, integrating fuzzy set and rough set theory together, extending equivalence relation to fuzzy relation, can deal with fuzzy-valued attributes. By analyzing three problems of FRAR which is a fuzzy decision table attribute reduction algorithm having extensive use, this paper proposes a new reduction algorithm which has better overcome the problem, can handle larger fuzzy decision table. Experimental results show that our reduction algorithm is much quicker than the FRAR algorithm.

Research to E-Commerce Customers Losing Predict Based on Rough Set

2011 ◽  
Vol 58-60 ◽  
pp. 164-170 ◽  
Author(s):  
Ming Jun Wang ◽  
Shu Xian Deng
Keyword(s):  
Data Analysis ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Customer Behavior ◽  
Purchase Behavior ◽  
Decision Table ◽  
Purchasing Behavior ◽  
Expert Opinions ◽  
Loss Prediction

The present paper based on rough set theory is to analyze the reason of an e-commerce customers losing. The e-commerce is virtual, customers purchase behavior is random, and there is the 20/80 theory. The focus to the e-commerce customers losing predict is to bring enterprise 80percent profits or frequent buying clients, they will be the study samples. Therefore, we must first find out these clients from numerous customers, analyze their purchasing behavior, and it is one of the important links loss prediction. This process may be realized by customer behavior data clustering. We have analyzed the data in one e-commerce database, and according to a certain algorithm has classified these customers, one kind is superior customers, one kind is general customers, the rest is temporary customers. And a lot of questionnaire survey have been done to these kinds of customers, and then combining e-commerce expert opinions formed the customers data analysis and decision table, then the algorithm, which is the decision table blindly delete attribute reduction algorithm, is adopted to process the attributes reduction to the decision table. Then, we get the reduction table of the customers’ data analysis and decision. According to the reduction table, we summarize e-commerce customers’ loss decision rule. Through these decision-making rules, we can predict these losing customers, and take timely measures necessary to retain.

Feature Extraction from Engine's Data Based on Rough Sets Theory

2011 ◽  
Vol 120 ◽  
pp. 410-413
Author(s):  
Feng Wang ◽  
Li Xin Jia
Keyword(s):  
Feature Extraction ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Technical Condition ◽  
Decision Table ◽  
Discernibility Matrix ◽  
Rough Sets Theory ◽  
Sets Theory

The speed signal of engine contains abundant information. This paper introduces rough set theory for feature extraction from engine's speed signals, and proposes a method of mining useful information from a mass of data. The result shows that the discernibility matrix algorithm can be used to reduce attributes in decision table and eliminate unnecessary attributes, efficiently extracted the features for evaluating the technical condition of engine.

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