scholarly journals Analisa Pola Belanja Swalayan Daily Mart Untuk Menentukan Tata Letak Barang Menggunakan Algoritma FP-Growth

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Kezia Sumangkut ◽  
Arie S.M. Lumenta ◽  
Virginia Tulenan
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Knowledge Discovery In Database ◽  
Mining Association Rules

Abstrak --- Perkembangan pasar modern yang semakin hari semakin pesat dapat dilihat dari pusat perbelanjaan seperti supermarket, minimarket, grosir, dan lain sebagainya yang dibangun untuk kebutuhan melayani konsumen. Dan pemanfaatan data transaksi yang banyak dapat memberikan pengetahuan yang menarik dalam membuat kebijakan dan strategi penempatan rak barang. Maraknya perbelanjaan modern dan pesaing bisnis seperti itu tidak lepas dari peralihan pola pikir konsumen yang tadinya mencari harga yang murah, kini sudah memperhatikan aspek keamanan, kebersihan, kenyamanan, keramahan dalam pelayanan serta kelengkapan jenis barang dan penempatan rak barang. Oleh karena itu dalam penelitian ini, penulis mengangkat permasalahan tentang Analisa Pola Belanja Swalayan Daily Mart Untuk Menentukan Tata Letak Barang Menggunakan Algoritma FP-Growth, dalam pelayanan yang sering terjadi di swalayan Daily Mart, dan  untuk mewujudkan hal itu penulis menerapkan metodologi KDD (Knowledge Discovery in Database). Salah satu teknik Data Mining dalam penelitian ini adalah Association Rule dalam Java Weka untuk mencari pengetahuan pola dari pembelian konsumen. Hasil dari penelitian ini berupa data pola pembelian/struk yang memiliki nilai confidence yang tinggi sebagai bahan untuk merekomendasi tata letak sesuai banyak barang yang paling sering dibeli. Kata Kunci --- Data Mining, Association Rules, Market Based Analysis, Java Weka

A Novel Efficient Mining Association Rules Algorithm for Distributed Databases

2010 ◽  
Vol 108-111 ◽  
pp. 50-56 ◽  
Author(s):  
Liang Zhong Shen
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Distributed Databases ◽  
New Method ◽  
Rule Mining ◽  
Mining Association Rules

Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate professionals, association rule mining is receiving increasing attention. The technology of data mining is applied in analyzing data in databases. This paper puts forward a new method which is suit to design the distributed databases.

XML-Enabled Association Analysis

2011 ◽  
pp. 2117-2122
Author(s):  
Ling Feng
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Marketing Strategy ◽  
Association Rules ◽  
Association Rule ◽  
Rule Mining ◽  
Xml Data ◽  
Market Basket ◽  
Transactional Databases ◽  
Mining Association Rules

The discovery of association rules from large amounts of structured or semi-structured data is an important data mining problem [Agrawal et al. 1993, Agrawal and Srikant 1994, Miyahara et al. 2001, Termier et al. 2002, Braga et al. 2002, Cong et al. 2002, Braga et al. 2003, Xiao et al. 2003, Maruyama and Uehara 2000, Wang and Liu 2000]. It has crucial applications in decision support and marketing strategy. The most prototypical application of association rules is market basket analysis using transaction databases from supermarkets. These databases contain sales transaction records, each of which details items bought by a customer in the transaction. Mining association rules is the process of discovering knowledge such as “80% of customers who bought diapers also bought beer, and 35% of customers bought both diapers and beer”, which can be expressed as “diaper ? beer” (35%, 80%), where 80% is the confidence level of the rule, and 35% is the support level of the rule indicating how frequently the customers bought both diapers and beer. In general, an association rule takes the form X ? Y (s, c), where X and Y are sets of items, and s and c are support and confidence, respectively. In the XML Era, mining association rules is confronted with more challenges than in the traditional well-structured world due to the inherent flexibilities of XML in both structure and semantics [Feng and Dillon 2005]. First, XML data has a more complex hierarchical structure than a database record. Second, elements in XML data have contextual positions, which thus carry the order notion. Third, XML data appears to be much bigger than traditional data. To address these challenges, the classic association rule mining framework originating with transactional databases needs to be re-examined.

scholarly journals Design and Implementation of a Generator of Large , Dense ,or Sparse Databases to Test Association Rules Miner

2002 ◽  
Vol 40 (1) ◽  
pp. 25-31
Author(s):  
Hussien Al-Khafaji ◽  
Alaa Al-Hamami ◽  
Abbas F. Abdul-Kader
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Rule Mining ◽  
System Generator ◽  
Qualitative And Quantitative ◽  
Design And Implementation ◽  
Knowledge Discovery In Database ◽  
Mining Algorithms ◽  
Association Rules Discovery

Association rules discovery has emerged as a very important problem in knowledge discovery in database and data mining. A number of algorithms is presented to mine association rules. There are many factors that affect the efficiency of rules mining algorithms, such as largeness, denances, and sparseness of databases used to be mined, in addition to number of items, number and average sizes of transactions, number and average sizes of frequent itemscts, and number and average sizes of potentially maximal itemsets. It is impossible to change present realworld catabase's characteristics to fairly test and determine the best and wurst cases of rule-mining algorithms. to be efficiently used for present and future databases. So the researchers attend to construct artificial database to qualitative and quantitative presence of the above mentioned factors to test the efficiency of rule mining algorithms and programs. The construction of such databases CATmes very large amount of the and efforts. This resent presents a software system, generator, to construct artificial databases.

Hiding Sensitive Association Rules by Sanitizing

2013 ◽  
Vol 694-697 ◽  
pp. 2317-2321
Author(s):  
Hui Wang
Keyword(s):  
Data Mining ◽  
Side Effects ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Mining Technology ◽  
Data Mining Techniques ◽  
Confidential Data ◽  
Knowledge Hiding ◽  
Existing Data

The goal of knowledge discovery is to extract hidden or useful unknown knowledge from databases, while the objective of knowledge hiding is to prevent certain confidential data or knowledge from being extracted through data mining techniques. Hiding sensitive association rules is focused. The side-effects of the existing data mining technology are investigated. The problem of sensitive association rule hiding is described formally. The representative sanitizing strategies for sensitive association rule hiding are discussed.

scholarly journals PENENTUAN ATURAN ASOSIASI PADA TRANSAKSI PEMINJAMAN BUKU MENGGUNAKAN ALGORITMA FP-GROWTH

2018 ◽  
Vol 9 (1) ◽  
pp. 15
Author(s):  
Elwani Elwani
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rule ◽  
Knowledge Discovery In Database

<p>Tujuan dari penelitian ini dapat membantu Perpustakaan STMIK – AMIK Dumai untuk mengambil kesimpulan menentukan jenis Buku yang paling banyak diminati oleh mahasiswa. Istilah Data mining dan knowledge discovery in database (KDD) sering kali digunakan secara bergantian untuk menjelaskan proses penggalian informasi tersembunyi dalam suatu basis data yang besar. Penelitian ini dilakukan untuk mempelajari Data mining merupakan proses untuk mendapatkan informasi yang berguna dari gudang basis data yang berupa ilmu pengetahuan. Penelitian ini melakukan analisa data dengan menggunakan Data mining dan metode algoritma FP-Growth dan Tools Rapidminer studio7.3. Algoritma FP-Growth menganalisis data transaksi peminjaman buku untuk mengetahui dalam perpustakaan. Hasil algoritma FP-Growth dapat menemukan rule atau knowledge untuk menganalisa strategi dalam menentukan transaksi peminjaman buku dan dapat digunakan untuk proses ekstraksi rule atau knowledge yang dihasilkan. Association rule adalah salah satu teknik utama dalam Data mining dan merupakan bentuk yang paling umum dipakai dalam menemukan pattern atau poladari suatu kumpulan data. Berdasarkan hasil pengujian dan analisa Assoction Rule menggunakan Algoritma FP-Growth dan Tools Rapidminer Studio 7.3. Jadi jumlah Rules keseluruhan yang telah diproses adalah 7 keputusan atau pengetahuan baru dengan nilai kombinasi 12 jenis buku, nilai Support A (%) terendah adalah 0,143 dengan Confidence ≥ 50 % “Yes” dan ≤ 50 % “No”.</p><p><br /><strong>Kata Kunci</strong>: Knowledge Discovery in Database (KDD), Data mining, Fp-Growth, Association rule,</p>

Mining Association Rules

2010 ◽  
pp. 647-673 ◽  
Author(s):  
Mihai Gabroveanu
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Knowledge Discovery In Databases ◽  
Discovery Process ◽  
Fuzzy Association Rules ◽  
Large Databases ◽  
Hidden Knowledge ◽  
Basic Concepts

During the last years the amount of data stored in databases has grown very fast. Data mining, also known as knowledge discovery in databases, represents the discovery process of potentially useful hidden knowledge or relations among data from large databases. An important task in the data mining process is the discovery of the association rules. An association rule describes an interesting relationship between different attributes. There are different kinds of association rules: Boolean (crisp) association rules, quantitative association rules, fuzzy association rules, etc. In this chapter, we present the basic concepts of Boolean and the fuzzy association rules, and describe the methods used to discover the association rules by presenting the most important algorithms.

Test Paper Generating Algorithm Based on Potential Weaknesses of Knowledge Points Mined by Association Rule

2013 ◽  
Vol 380-384 ◽  
pp. 2757-2760
Author(s):  
Yin Huang Le ◽  
Man Yang ◽  
Min Wu
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Test Paper ◽  
Suitable Test ◽  
Current Test ◽  
Mining Association Rules ◽  
Generating System

According to the problem that the flexibility and efficiency are not high of the current test paper generating system, applying data mining to figure out the inter and intra connection among all the knowledge points of CET-4, mining association rules, and picking out as more potential weaknesses of knowledge points as it can, then selecting items corresponding to the weaknesses and generating a user-level specific suitable test paper. Experiments show that the new test paper generating algorithm (TPGA) based on association rules response intelligently and quickly to different user levels, and provide an essential improvement in test paper generating process for users own good to enhance their exercise results.

Analisis Data Pembayaran Kredit Nasabah Bank Menggunakan Metode Data Mining

2013 ◽  
Vol 4 (1) ◽  
pp. 18-27
Author(s):  
Ira Melissa ◽  
Raymond S. Oetama
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Database

Data mining adalah analisis atau pengamatan terhadap kumpulan data yang besar dengan tujuan untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara yang lebih mudah dimengerti dan bermanfaat bagi pemilik data. Data mining merupakan proses inti dalam Knowledge Discovery in Database (KDD). Metode data mining digunakan untuk menganalisis data pembayaran kredit peminjam pembayaran kredit. Berdasarkan pola pembayaran kredit peminjam yang dihasilkan, dapat dilihat parameter-parameter kredit yang memiliki keterkaitan dan paling berpengaruh terhadap pembayaran angsuran kredit. Kata kunci—data mining, outlier, multikolonieritas, Anova

Sign in / Sign up

Export Citation Format

Share Document