Improved Algorithm for Mining Maximum Frequent Patterns Based on FP-Tree

2013 ◽  
Vol 756-759 ◽  
pp. 3692-3695 ◽  
Author(s):  
Nai Li Liu ◽  
Lei Ma
Keyword(s):  
Data Mining ◽  
Fast Algorithm ◽  
Association Rule ◽  
Frequent Patterns ◽  
Memory Space ◽  
The Cost ◽  
Mining Association Rule ◽  
Very High ◽  
Improved Algorithm

Mining association rule is an important matter in data mining, in which mining maximum frequent patterns is a key problem. Many of the previous algorithms mine maximum frequent patterns by producing candidate patterns firstly, then pruning. But the cost of producing candidate patterns is very high, especially when there exists long patterns. In this paper, the structure of a FP-tree is improved, we propose a fast algorithm based on FP-Tree for mining maximum frequent patterns, the algorithm does not produce maximum frequent candidate patterns and is more effectively than other improved algorithms. The new FP-Tree is a one-way tree and only retains pointers to point its father in each node, so at least one third of memory is saved. Experiment results show that the algorithm is efficient and saves memory space.

scholarly journals FAST ALGORITHM FOR MINING ASSOCIATION RULE

2006 ◽  
Vol 34 (1) ◽  
pp. 79-87
Author(s):  
M. H. Margahny ◽  
A. Shakour
Keyword(s):  
Fast Algorithm ◽  
Association Rule ◽  
Mining Association Rule

Visual Data Mining for Discovering Association Rules

2008 ◽  
pp. 2105-2120
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

Association Rule Mining in Collaborative Filtering

2017 ◽  
pp. 159-179 ◽  
Author(s):  
Carson K.-S. Leung ◽  
Fan Jiang ◽  
Edson M. Dela Cruz ◽  
Vijay Sekar Elango
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
Association Rules ◽  
Data Structures ◽  
Association Rule ◽  
Association Rule Mining ◽  
Real Life ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Association Rule Miner

Collaborative filtering uses data mining and analysis to develop a system that helps users make appropriate decisions in real-life applications by removing redundant information and providing valuable to information users. Data mining aims to extract from data the implicit, previously unknown and potentially useful information such as association rules that reveals relationships between frequently co-occurring patterns in antecedent and consequent parts of association rules. This chapter presents an algorithm called CF-Miner for collaborative filtering with association rule miner. The CF-Miner algorithm first constructs bitwise data structures to capture important contents in the data. It then finds frequent patterns from the bitwise structures. Based on the mined frequent patterns, the algorithm forms association rules. Finally, the algorithm ranks the mined association rules to recommend appropriate merchandise products, goods or services to users. Evaluation results show the effectiveness of CF-Miner in using association rule mining in collaborative filtering.

Visual Data Mining for Discovering Association Rules

2011 ◽  
pp. 209-226
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

Data Mining Pembentukan Pola Penggunaan Alat Kontrasepsi Dengan Metode Association Rule

2020 ◽  
Vol 4 (1) ◽  
pp. 186-191
Author(s):  
Elisa Hafrida ◽  
◽  
Febrina Sari ◽  
Desyanti Desyanti ◽  
Siti Nurjannah ◽  
...  
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Item ◽  
Mining Association Rule

Penggunaan Alat Kontrasepsi secara berkelanjutan merupakan faktor yang mempengaruhi keberhasilan Program Keluarga Berencana (KB). Seperti yang diketahui tidak semua alat kontrasepsi cocok dengan kondisi setiap orang, oleh karenanya setiap pribadi harus bisa memilih alat kontrasepsi yang cocok untuk dirinya. Permasalahannya banyak para wanita sulit untuk menentukan pilihan alat kontrasepsi yang akan digunakan, selain kurangnya pengetahuan dan informasi, Sampai saat ini belum ada konsep atau Pola untuk pemilihan alat kontrasepsi. Tujuan dari penelitian ini adalah Menemukan pola penggunaan alat kontrasepsi dengan menggunakan metode Data Mining Association Rule. Hasil kinerja Algoritma Apriori menghasilkan pola kombinasi yang menggambarkan kumpulan frequent item set dengan nilai confidence tertinggi yakni sebesar 90% pada Rule Jika Alat Kontrasepsi Suntik 3 Bulan Maka Usia Ibu 17-35 Tahun. Pola yang terbentuk merupakan hasil formulasi konsep, sehingga pola ini dapat dijadikan acuan bagi para calon akseptor dalam menentukan pilihan alat kontrasepsi yang cocok untuk digunakan.

Application of Association Rules Based on CF Gene in Intrusion Detection

2014 ◽  
Vol 556-562 ◽  
pp. 2603-2606
Author(s):  
Rong Fu ◽  
Li Yan Liu ◽  
Ying Qian Zhang ◽  
Yi He
Keyword(s):  
Intrusion Detection ◽  
Association Rules ◽  
Association Rule ◽  
Detection Technology ◽  
Mining Association Rule ◽  
Improved Algorithm

By analyzing and studying the most current algorithms about mining association rule, the rules evaluated by minimum confidence could not ensure the validity of the rules and will generate unrelated rules which will affect the intrusion detection work. This paper proposes CF measure based on the previous work and applies the association rule algorithm based on CF to intrusion detection technology to detect the intrusion behaviors in the network. Finally, experiments show that improved algorithm is more efficient.

Analysis and Research of Intrusion Detection System Based on Association Rule

2013 ◽  
Vol 709 ◽  
pp. 628-631
Author(s):  
Ya Bing Jiao
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Association Rule ◽  
Intrusion Detection System ◽  
Detection Efficiency ◽  
Detection System ◽  
Mining Association Rule

A model of intrusion detection system based on the technology data mining is presented on the basis of introduction on the concept and the technical method of the intrusion detection system. In this model, the two methods of the technology data mining association rule and the classified analysis cooperate with each other and the detection efficiency will be greatly enhanced.

scholarly journals ANALISIS TINGKAT KEJAHATAN ANAK DIBAWAH UMUR MENGGUNAKAN METODE FP-GROWTH (STUDI KASUS DI POLRESTA PADANG)

2020 ◽  
Vol 1 (1) ◽  
pp. 16-23
Author(s):  
Randika Farike Bania
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Itemset ◽  
Mining Association Rule

 Kekerasan terhadap anak sebagai setiap tindakan atau serangkaian tindakan wali atau kelalaian oleh orang tua atau pengasuh lainnya yang dihasilkan dapat membahayakan, atau berpotensi bahaya, atau memberikan ancaman yang berbahaya kepada anak..  Mengimplementasikan Data Mining, Association Rule dan Algoritma FP-Growth pada kekerasan kekerasan pada anak di bawah umur  untuk mengekstrak ilmu pengetahuan, informasi penting dan menarik dari database. Sumber  data yang digunakan masih merupakan data mentah yang belum diolah dan merupakan data kekerasan pada anak di bawah umur yang mencangkup laporan  di Polresta Padang. Hasil penelitian ini adalah berupa suatu perangkat lunak dengan mengimplementasikan algoritma FP-Growth yang menggunakan konsep pembangunan FP-Tree dalam mencari Frequent Itemset dan unutk pengujian hasil dilakukan dengan aplikasi yang telah dirancang menggunakan bahasa pemogramman PHP MYSQL. Hasil  pengujian didapatkan dari assosiasi kasus kekerasan yang dominan terjadi pada anak dibawah umur, yaitu jika Kasus Penganiayaan maka Korbannya Pelajar dengan nilai support 30% dan nilai confidence 84%, jika Pelaku Swasta maka Korban Pelajar dengan nilai support 20% dan confidence 73%, jika Pelaku Swasta, Kasus Penganiayaan maka Korban Pelajar dengan nilai support 17% dan confidence 71%, jika Kasus Cabul maka Korban Pelajar dengan nilai support 28% dan confidence 65% dan jika Pelaku Pengangguran maka Korban pelajar dengan nilai support 17% dan confidence 64%.

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