Accelerating Support Count for Association Rule Mining on GPUs

2016 ◽  
Author(s):  
Vasileios Zois ◽  
Anand Panangadan ◽  
Viktor Prasanna
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Support Count

scholarly journals Coalesce based binary table: an enhanced algorithm for mining frequent patterns

2017 ◽  
Vol 7 (1.5) ◽  
pp. 51
Author(s):  
M. Sireesha ◽  
Srikanth Vemuru ◽  
S. N. TirumalaRao
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
The Other ◽  
Frequent Patterns ◽  
Discovery Process ◽  
Rule Mining ◽  
Support Count ◽  
Frequent Items ◽  
The Given

Frequent item set mining and association rule mining is the key tasks in knowledge discovery process. Various customized algorithms are being implemented in Association Rule Mining process to find the set of frequent patterns. Though we have many algorithms apriori is one of the standard algorithm for finding frequent itemsets, but this algorithm is inefficient because of several scans of database and more number of candidates to be generated. To overcome these limitations, in this paper a new algorithm called Coalesce based Binary Table is introduced. Through this algorithm the given database is scanned only once to generate Binary Table by which frequent-1 itemsets are found.  To progress the process, infrequent-1 itemsets are identified and removed from the Binary Table to rearrange the items in support ascending order. To each frequent-1 itemset find Coalesce matrix and Index List to generate all frequent itemsets having the same support count as representative items and the remaining frequent itemsets are obtained in depth first manner. The significant benefits with the proposed method are the whole database is scanned only once, no need to generate and check each candidate to find the set of frequent items. On the other hand frequent items having the same support counts as representative items can be identified directly by joining the representative item with all the combinations of Coalesce matrix. So, it is proven that coalesce based Binary Table is panacea to cut short the time in identifying the frequent itemsets hence the efficiency is improved.

Prediksi Code Defect Perangkat Lunak Dengan Metode Association Rule Mining dan Cumulative Support Thresholds

2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Rizal Setya Perdana ◽  
Umi Laili Yuhana
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Program Code

Kualitas perangkat lunak merupakan salah satu penelitian pada bidangrekayasa perangkat lunak yang memiliki peranan yang cukup besar dalamterbangunnya sistem perangkat lunak yang berkualitas baik. Prediksi defectperangkat lunak yang disebabkan karena terdapat penyimpangan dari prosesspesifikasi atau sesuatu yang mungkin menyebabkan kegagalan dalam operasionaltelah lebih dari 30 tahun menjadi topik riset penelitian. Makalah ini akandifokuskan pada prediksi defect yang terjadi pada kode program (code defect).Metode penanganan permasalahan defect pada kode program akan memanfaatkanpola-pola kode perangkat lunak yang berpotensi menimbulkan defect pada data setNASA untuk memprediksi defect. Metode yang digunakan dalam pencarian polaadalah memanfaatkan Association Rule Mining dengan Cumulative SupportThresholds yang secara otomatis menghasilkan nilai support dan nilai confidencepaling optimal tanpa membutuhkan masukan dari pengguna. Hasil pengujian darihasil pemrediksian defect kode perangkat lunak secara otomatis memiliki nilaiakurasi 82,35%.

An Imputation Technique for Missing Context Data Based on Spatial-temporal and Association Rule Mining

2011 ◽  
Vol 32 (12) ◽  
pp. 2913-2918 ◽  
Author(s):  
Yu-xiang Wang ◽  
Xiu-quan Qiao ◽  
Xiao-feng Li ◽  
Luo-ming Meng
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Context Data
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