Association rule based frequent pattern mining in biological sequences

The patterns generated by frequent pattern mining aims to find the frequent items without considering the utilities of the different items. The traditional association rule mining treats all items to be of equal utility. This is not always the case for a real world application. Utility based data mining is a new area of research and is complementing the frequency based approach. The main objective of Utility Mining is to identify the item sets with highest utilities, by considering profit, quantity, cost or other user preferences as the Utility of the item. Recent approaches developed so far considers the utilities of items to be same over a particular period of time. In our approach we have proposed that the utility of items vary over a period of time. Our work also proposed that the utility of items may also assume negative values. Our work thus treats the data mining in more realistic manner

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PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA APRIORI UNTUK MENENTUKAN POLA GOLONGAN PENYANDANG MASALAH KESEJAHTERAAN SOSIAL

Sebatik ◽

10.46984/sebatik.v26i1.1622 ◽

2022 ◽

Vol 26 (1) ◽

Author(s):

Irwan Adji Darmawan ◽

Muhammad Fakhri Randy ◽

Imam Yunianto ◽

Muhamad Malik Mutoffar ◽

M Tio Putra Salis

Keyword(s):

Data Mining ◽

Association Rule ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Itemset ◽

Frequent Pattern ◽

Data Set ◽

Minimum Support

Penyandang Masalah Kesejahteraan Sosial (PMKS) menjadi satu dari sekian masalah yang terdapat di daerah perkotaan, sebab dapat mengganggu pembangunan kota, ketertiban umum, keamanan dan stabilitas. Sejauh ini langkah yang dilakukan sementara masih terfokus dengan cara penanganan PMKS, masih belum mengarah untuk mencegah. Menentukan pola golongan PMKS merupakan salah satu cara yang dapat dilakukan. Algoritma Apriori memiliki fungsi untuk membantu menemukan pola yang terdapat pada data (frequent pattern mining) untuk menentukan frequent itemset yang menggunakan metode Association Rule dalam data mining. Dalam penghitungan secara manual yang dilakukan maka didapat pola kombinasi antara lain 3 rules yang memiliki nilai minimum support 15% dengan confidence tertinggi 100% menggunakan Algoritma Apriori. Dalam menguji Algoritma Apriori digunakan aplikasi RapidMiner. RapidMiner merupakan satu dari beberapa software pengolah data mining, misalnya menganalisis teks, mengekstrak pola data set kemudian dikombinasikan menggunakan metode statistik, database, dan kecerdasan buatan agar didapat informasi yang tinggi berasal dari olahan data. Hasil yang didapat dari pengujian perbandingan pola antar golongan PMKS. Dari pengujian menggunakan aplikasi RapidMiner dan penghitungan secara manual Algoritma Apriori, maka disimpulkan dengan kriteria pengujian, bahwa pola (rules) golongan dengan nilai confidence (c) penghitungan manual Algoritma Apriori dapat dibilang tidak mendekati hasil pengujian aplikasi RapidMiner, maka dapat dikatakan tingkat keakuratan pengujian rencah, hanya 37,5%.

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Penerapan Data Mining Menggunakan Algoritma Apriori untuk Menentukan Pola Penyebab Gelandangan dan Pengemis

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020721376 ◽

2020 ◽

Vol 7 (2) ◽

pp. 229

Author(s):

Wirta Agustin ◽

Yulya Muharmi

Keyword(s):

Data Mining ◽

Association Rule ◽

Urban Areas ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Itemset ◽

Frequent Pattern ◽

Data Sets ◽

Apriori Algorithm ◽

Data Set

Gelandangan dan pengemis salah satu masalah yang ada di daerah perkotaan, karena dapat mengganggu ketertiban umum, keamanan, stabilitas dan pembangunan kota. Upaya yang dilakukan saat ini masih fokus pada cara penanganan gelandangan dan pengemis, belum untuk pencegahan. Salah satu cara yang bisa dilakukan adalah dengan menentukan pola usia gelandangan dan pengemis. Algoritma Apriori sebuah metode Association Rule dalam data mining untuk menentukan frequent itemset yang berfungsi membantu menemukan pola dalam sebuah data (frequent pattern mining). Perhitungan manual menggunakan algoritma apriori, menghasilkan pola kombinasi sebanyak 3 rules dengan nilai minimum support sebesar 30% dan nilai confidence tertinggi sebesar 100%. Pengujian penerapan Algoritma Apriori menggunakan aplikasi RapidMiner. RapidMiner salah satu software pengolahan data mining, diantaranya analisis teks, mengekstrak pola-pola dari data set dan mengkombinasikannya dengan metode statistika, kecerdasan buatan, dan database untuk mendapatkan informasi bermutu tinggi dari data yang diolah. Hasil pengujian menunjukkan perbandingan pola usia gelandangan dan pengemis yang berpotensi menjadi gelandangan dan pengemis. Berdasarkan hasil pengujian aplikasi RapidMiner dan hasil perhitungan manual Algoritma Apriori, dapat disimpulkan sesuai kriteria pengujian, bahiwa pola (rules) usia dan nilai confidence (c) hasil perhitungan manual Algoritma Apriori tidak mendekati nilai hasil pengujian menggunakan aplikasi RapidMiner, maka tingkat keakuratan pengujian rendah, yaitu 37.5 %. Abstract Homeless and beggars are one of the problems in urban areas as they possibly disrupt public order, security, stability and urban development. The efforts conducted are still focusing on managing the existing homeless and beggars instead of preventing the potential ones. One of the methods used for solving this problem is Algoritma Apriori which determines the age pattern of homeless and beggars. Apriori Algorithm is an Association Rule method in data mining to determine frequent item set that serves to help in finding patterns in a data (frequent pattern mining). The manual calculation through Apriori Algorithm obtains combination pattern of 3 rules with a minimum support value of 30% and the highest confidence value of 100%. These patterns were refences for the incharged department in precaution action of homeless and beggars arising numbers. Apriori Algorithm testing uses the RapidMiner application which is one of data mining processing software, including text analysis, extracting patterns from data sets and combining them with statistical methods, artificial intelligence, and databases to obtain high quality information from processed data. Based on the results of the said testing, it can be concluded that the level of accuracy test is low, i.e. 37.5%.

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ENHANCED RECONFIGURABLE WEIGHTED ASSOCIATION RULE MINING FOR FREQUENT PATTERNS OF WEB LOGS

International Journal of Computing ◽

10.47839/ijc.13.2.624 ◽

2014 ◽

pp. 97-105

Author(s):

SP. Malarvizhi ◽

B. Sathiyabhama

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Reconfigurable Architecture ◽

Frequent Pattern ◽

Web Pages ◽

Rule Mining ◽

Frequent Item ◽

Mining Algorithms

Systolic tree structure is a reconfigurable architecture in Field-programmable gate arrays (FPGA) which provide performance advantages. It is used for frequent pattern mining operations. High throughput and cost effective performance are the highlights of the systolic tree based reconfigurable architecture. Frequent pattern mining algorithms are used to find frequently occurring item sets in databases. However, space and computational time requirements are very high in frequent pattern mining algorithms. In the proposed system, systolic tree based hardware mechanism is employed with Weighted Association Rule Mining (WARM) for frequent item set extraction process of the Web access logs. Weighted rule mining is to mine the items which are assigned with weights based on user’s interest and the importance of the items. In the proposed system, weights are assigned automatically to Web pages that are visited by the users. Hence, systolic tree based rule mining scheme is enhanced for WARM process, which fetches the frequently accessed Web pages with weight values. The dynamic Web page weight assignment scheme uses the page request count and span time values. The proposed system improves the weight estimation process with span time, request count and access sequence details. The user interest based page weight is used to extract the frequent item sets. The proposed system will also improve the mining efficiency on sparse patterns. The goal is to drive the mining focus to those significant relationships involving items with significant weights.

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Social Networking Data Research Using Frequent Pattern Mining and Machine Learning Data

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9352.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 4784-4789

Keyword(s):

Social Networking ◽

Association Rule ◽

Association Rule Mining ◽

Pattern Mining ◽

Performance Metrics ◽

Data Extraction ◽

Frequent Pattern Mining ◽

Extraction Process ◽

Frequent Pattern ◽

Rule Mining

The data are generated by the sources are very large in number with variety of form. These data are organized in to specific format in order to handle properly. Data mining methods are addressed various problem during data extraction process to analytical process. The relevant data are extracted by applying pattern over the huge databases. Association rule mining introduces the method to extracts the related data from the datasets using the performance metrics like support and confidence. Traditional algorithm uses this metrics which is restricted to common attribute format. This problem is addressed by using generic attribute format with frequent pattern mining. The main objective of the paper is to analyze the algorithm and performance metrics related to the frequent patter mining or relevant data. Association rule mining has analyzed with various parameters in single connectivity and multi connectivity rules. Social networking suffers various problem because of uncertain data arrived for processing which is analyzed with various efficiency related elements. The analysis and prediction are also compared with the machine algorithms like classification and clustering and so on. Various frequent pattern mining algorithm is analyzed and review has been carried out based on the performance level.

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An Adaptive Data Distribution Through Tree Rules in Frequent Pattern Mining

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit183894 ◽

2018 ◽

pp. 300-305

Keyword(s):

Information Sharing ◽

Pattern Mining ◽

Data Distribution ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

General Development ◽

Secure Information ◽

Evaluation Parameters ◽

Secure Information Sharing

Information sharing among the associations is a general development in a couple of zones like business headway and exhibiting. As bit of the touchy principles that ought to be kept private may be uncovered and such disclosure of delicate examples may impacts the advantages of the association that have the data. Subsequently the standards which are delicate must be secured before sharing the data. In this paper to give secure information sharing delicate guidelines are bothered first which was found by incessant example tree. Here touchy arrangement of principles are bothered by substitution. This kind of substitution diminishes the hazard and increment the utility of the dataset when contrasted with different techniques. Examination is done on certifiable dataset. Results shows that proposed work is better as appear differently in relation to various past strategies on the introduce of evaluation parameters.

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