An algorithm of multi-level fuzzy association rules mining with multiple minimum supports in network faults diagnosis

2013 ◽  
Author(s):  
Pan Liu ◽  
Xing-ming Li ◽  
Yan-qing Feng
Keyword(s):  
Association Rules ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Multi Level ◽  
Faults Diagnosis ◽  
Multiple Minimum Supports

Different Types of Association Rules Mining Review

2012 ◽  
Vol 241-244 ◽  
pp. 1589-1592
Author(s):  
Jun Tan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Weighted Association Rules ◽  
Application Systems ◽  
Different Types ◽  
Multi Level

In recent years, many application systems have generate large quantities of data, so it is no longer practical to rely on traditional database technique to analyze these data. Data mining offers tools for extracting knowledge from data, leading to significant improvement in the decision-making process. Association rules mining is one of the most important data mining technology. The paper first presents the basic concept of association rule mining, then discuss a few different types of association rules mining including multi-level association rules, multidimensional association rules, weighted association rules, multi-relational association rules, fuzzy association rules.

A New Algorithm for the Fuzziness of Alarms in Network Faults Diagnosis

2012 ◽  
Vol 198-199 ◽  
pp. 1539-1544 ◽  
Author(s):  
Pan Liu ◽  
Xing Ming Li ◽  
Jian Wu
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Communication Networks ◽  
Association Rules ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Edge Field ◽  
Alarm Correlation ◽  
Faults Diagnosis ◽  
Network Fault

The alarm correlation analysis based on fuzzy association rules mining is the popular and cutting-edge field of the network fault diagnosis research. In the application environment of alarms in communication networks, a new algorithm of the fuzziness of alarms which is called FKMA (Fuzzy K-Means of Alarms algorithm) is proposed .During the process of fuzziness, there are two methods of sorting the center. Simulations are carried out to the comparison of the two methods. The fuzziness of alarms is effectively realized. And fuzzy association rules mining are achieved. The advantages and efficiency of FKMA are demonstrated by experiments.

Evaluation of sustainability using fuzzy association rules mining

2011 ◽  
Vol 13 (6) ◽  
pp. 809-819 ◽  
Author(s):  
S. Vinodh ◽  
K. Eazhil Selvan ◽  
N. Hari Prakash
Keyword(s):  
Association Rules ◽  
Association Rules Mining ◽  
Fuzzy Association Rules

scholarly journals ATURAN REKOMENDASI BARANG MENGGUNAKAN MULTI LEVEL ASSOCIATION RULES MINING (ML-ARM)

Transmisi ◽  
2018 ◽  
Vol 20 (2) ◽  
pp. 49
Author(s):  
Zahra Arwananing Tyas
Keyword(s):  
Association Rules ◽  
Association Rules Mining ◽  
Multi Level ◽  
F Measure

Sistem rekomendasi dapat menghasilkan rekomendasi dengan berbagai cara dan menggunakan berbagai macam metode, salah satunya adalah memanfaatkan tumpukan kasus lama atau tumpukan data transaksi lama yang dapat menghasilkan informasi atau aturan dengan metode Association Rules Mining(ARM). Aturan terbentuk dengan metode multi level ARM dan menghasilkan 5 aturan yang akan dicocokkan dengan masukan pengguna. Saat aturan ditemukan cocok maka consequent dari aturan tersebut akan dijadikan hasil rekomendasi.  Hasil pengujian dari aturan yang terbentuk memiliki nilai akurasi 94,12% dan nilai precision, recall dan F-measure untuk sistem rekomendasi ini pada proses rekomendasi dengan aturan yaitu berturut 0,475; 0,513 dan 0,25.

Extracting relations of crime rates through fuzzy association rules mining

2019 ◽  
Vol 50 (2) ◽  
pp. 448-467 ◽  
Author(s):  
Zhongjie Zhang ◽  
Jian Huang ◽  
Jianguo Hao ◽  
Jianxing Gong ◽  
Hao Chen
Keyword(s):  
Association Rules ◽  
Crime Rates ◽  
Association Rules Mining ◽  
Fuzzy Association Rules

Applying cluster-based fuzzy association rules mining framework into EC environment

2012 ◽  
Vol 12 (8) ◽  
pp. 2114-2122 ◽  
Author(s):  
Hung-Pin Chiu ◽  
Yi-Tsung Tang ◽  
Kun-Lin Hsieh
Keyword(s):  
Association Rules ◽  
Association Rules Mining ◽  
Fuzzy Association Rules

Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 842-845 ◽  
Author(s):  
Jia Mei Guo ◽  
Yin Xiang Pei
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Clustering Algorithm ◽  
Original Data ◽  
Data Set ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Redundant Data ◽  
Fuzzy Partitions ◽  
Rules Extraction

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

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