scholarly journals Proposed Design for Data Retrieval using Efficient Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 6331-6335
Keyword(s):  
Data Mining ◽  
Efficient Algorithm ◽  
Hybrid Algorithm ◽  
Accurate Result ◽  
Data Retrieval ◽  
Transaction Data ◽  
Customer Purchases ◽  
Practical Analysis ◽  
High Utility ◽  
Very High

Data mining is used for finding patterns from large amount of data which is in raw format. These patterns are then analyzed to gain useful information from them. There are many branches of data mining, one of the most interesting branch is frequent item-set mining (FIM). FIM deals with finding items that are frequently brought together by customers. Like for example, if a customer purchases a mobile phone, he also tends to purchase mobile cover, ear phones etc along with it. But such kinds of patterns are not always useful to all stake-holders. Such patterns do not emphasize on the profit obtained of sale i.e. the utility obtained from product. In order to overcome this problem, the concept of high utility item-set mining (HUIM) came into existence. HUIM is used to find the utility or profit obtained from the items in transaction data. There are various algorithms for HUIM, TKU (Top K Utility) and TKO (Top K in one phase) are two well known algorithms of HUIM. The detailed study and practical analysis of these two algorithms show that there are certain drawbacks assigned with them. TKO algorithm gets executed in very less amount of time but it gives incorrect output. Whereas TKU algorithm gives accurate results when applied on database, but its execution time is very high. Hence in order to enhance the performance of these two HUIM algorithms a hybrid algorithm i.e. TKO with TKU algorithm is proposed in this paper. The two algorithms when combined give accurate result and also get executed in considerable less amount of time

Research on Structure and Model of E-Commerce Based on Web Mining

2010 ◽  
Vol 143-144 ◽  
pp. 338-342
Author(s):  
Xu Hao
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
High Speed ◽  
Web Mining ◽  
Data Sets ◽  
Individual Data ◽  
Specific Domain ◽  
Transaction Data ◽  
The Cost ◽  
Very High

As one of the key applied forms on Internet, E-commerce has been growing at a very high speed in China in recent years. Starting from the perspective that E-commerce consumers pursue low price while retailers try to decrease the cost. Data mining is a form of knowledge discovery essential for solving problems in a specific domain. Individual data sets may be gathered and studied collectively for purposes other than those for which they were originally created. Data mining is most commonly used in attempts to induce association rules from transaction data.

Efficient Algorithm for Mining High Utility Pattern Considering Length Constraints

2019 ◽  
Vol 15 (3) ◽  
pp. 1-27
Author(s):  
Kuldeep Singh ◽  
Bhaskar Biswas
Keyword(s):  
Data Mining ◽  
Upper Bound ◽  
Efficient Algorithm ◽  
State Of The Art ◽  
Memory Usage ◽  
Important Data ◽  
Benchmark Datasets ◽  
Projection Techniques ◽  
High Utility ◽  
High Utility Itemsets

High utility itemset (HUI) mining is one of the popular and important data mining tasks. Several studies have been carried out on this topic, which often discovers a very large number of itemsets and rules, which reduces not only the efficiency but also the effectiveness of HUI mining. In order to increase the efficiency and discover more interesting HUIs, constraint-based mining plays an important role. To address this issue, the authors propose an algorithm to discover HUIs with length constraints named EHIL (Efficient High utility Itemsets with Length constraints) to decrease the number of HUIs by removing tiny itemsets. EHIL adopts two new upper bound named sub-tree and local utility for pruning and modify them by incorporating length constraints. To reduce the dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. The execution time improvements ranged from a modest five percent to two orders of magnitude across benchmark datasets. The memory usage is up to twenty-eight times less than state-of-the-art algorithm FHM+.

scholarly journals Efficient Algorithm for Mining High Utility Item Sets

2019 ◽  
Vol 8 (6S2) ◽  
pp. 656-659
Keyword(s):  
Data Mining ◽  
Efficient Algorithm ◽  
Tree Structure ◽  
Utility Models ◽  
High Utility ◽  
Mining Capacity

Efficient introduction of obvious things in savage datasets could be a key test for data mining. Assorted perspective for making high utility models have been held for the instigating years, and this raises different issues, for instance, the age of a more perceivable than common level of contender things for top utility things, and clearly wealth mining capacity to the degree speed and zone. The unessential tree structure that has beginning late been organized, i.e., FP-Tree and UP-Tree, holds information on get-together advancement and itemsets, mining results, and dependably abstains from checking the affirmed data. During this report to get a controlled far up-tree is seen, basically twofold checks the data to get the up-and-comer.

Pemanfaatan Metode K-Means Dalam Penentuan Persediaan Barang

2018 ◽  
Vol 6 (1) ◽  
pp. 41-48
Author(s):  
Santoso Setiawan
Keyword(s):  
Data Mining ◽  
Stock Management ◽  
Transaction Data ◽  
Business People ◽  
Cluster Data ◽  
Data Clusters ◽  
Inventory Strategy ◽  
Transactional Data

Abstract   Inaccurate stock management will lead to high and uneconomical storage costs, as there may be a void or surplus of certain products. This will certainly be very dangerous for all business people. The K-Means method is one of the techniques that can be used to assist in designing an effective inventory strategy by utilizing the sales transaction data that is already available in the company. The K-Means algorithm will group the products sold into several large transactional data clusters, so it is expected to help entrepreneurs in designing stock inventory strategies.   Keywords: inventory, k-means, product transaction data, rapidminer, data mining   Abstrak   Manajemen stok yang tidak akurat akan menyebabkan biaya penyimpanan yang tinggi dan tidak ekonomis, karena kemungkinan terjadinya kekosongan atau kelebihan produk tertentu. Hal ini sangat berbahaya bagi para pelaku bisnis. Metode K-Means adalah salah satu teknik yang dapat digunakan untuk membantu dalam merancang strategi persediaan yang efektif dengan memanfaatkan data transaksi penjualan yang telah tersedia di perusahaan. Algoritma K-Means akan mengelompokkan produk yang dijual ke beberapa cluster data transaksi yang umumnya besar, sehingga diharapkan dapat membantu pengusaha dalam merancang strategi persediaan stok.   Kata kunci: data transaksi produk, k-means, persediaan, rapidminer, data mining.

A data mining algorithm for fuzzy transaction data

2013 ◽  
Vol 48 (6) ◽  
pp. 2963-2971 ◽  
Author(s):  
Chin-Yuan Chen ◽  
Gin-Shuh Liang ◽  
Yuhling Su ◽  
Mao-Sheng Liao
Keyword(s):  
Data Mining ◽  
Data Mining Algorithm ◽  
Transaction Data ◽  
Mining Algorithm

A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

2011 ◽  
Vol 145 ◽  
pp. 292-296
Author(s):  
Lee Wen Huang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Execution Time ◽  
Hybrid Algorithm ◽  
Hybrid Approach ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Large Databases ◽  
Closed Itemsets ◽  
Simulation Results

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

scholarly journals A Scalable Approach for Data Mining – AHUIM

Webology ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 92-103
Author(s):  
Vandna Dahiya ◽  
Sandeep Dalal
Keyword(s):  
Data Mining ◽  
Research Paper ◽  
Large Datasets ◽  
Novel Technique ◽  
Itemset Mining ◽  
Essential Form ◽  
High Utility

Utility itemset mining, which finds the item sets based on utility factors, has established itself as an essential form of data mining. The utility is defined in terms of quantity and some interest factor. Various methods have been developed so far by the researchers to mine these itemsets but most of them are not scalable. In the present times, a scalable approach is required that can fulfill the budding needs of data mining. A Spark based novel technique has been recommended in this research paper for mining the data in a distributed way, called as Absolute High Utility Itemset Mining (AHUIM). The technique is suitable for small as well as large datasets. The performance of the technique is being measured for various parameters such as speed, scalability, and accuracy etc.

scholarly journals ANALISIS POLA PENJUALAN DENGAN MENGGUNAKAN ALGORITMA APRIORI PADA KOPERASI KARYAWAN YAYASAN ANAKKU

2019 ◽  
Vol 2 (1) ◽  
pp. 31-36
Author(s):  
Arfianto Darmawan ◽  
Titin Kristiana
Keyword(s):  
Data Mining ◽  
Strong Association ◽  
Apriori Algorithm ◽  
Business Unit ◽  
Savings And Loans ◽  
Transaction Data ◽  
Existing Problems ◽  
A Value ◽  
Transaction Database ◽  
Using Data

The Anakku Foundation Cooperative is a multi-business cooperative consisting of shop businesses, savings and loans, and student shuttle services. Every sale of stuff services will be inputted data directly to each business unit. The Anakku Foundation Cooperative still has problems, including store transactions that cannot yet answer what items are often sold, when stock items are still difficult to determine the items that are still available or almost running out. Data mining techniques have been mostly used to overcome existing problems, one of which is the application of the Apriori algorithm to obtain information about the associations between products from a transaction database. Transaction data on school equipment sales at Cooperative Employees of Anakku Foundation can be reprocessed using Data mining applications so as to produce strong association rules between itemset sales of school supplies so that they can provide recommendations for item alignment and simplify the arrangement or strong item placement related to interdependence. The results are found that the highest value of support and confidence is if buying MUSLIM L1.5P1, so it would buy AL-IZHAR II LOGO with a value of 14.5% support and 79.5% confidence

scholarly journals IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN PADA PERUSAHAAN RETAIL

2019 ◽  
Vol 15 (1) ◽  
pp. 85-90 ◽  
Author(s):  
Jordy Lasmana Putra ◽  
Mugi Raharjo ◽  
Tommi Alfian Armawan Sandi ◽  
Ridwan Ridwan ◽  
Rizal Prasetyo
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Apriori Algorithm ◽  
Business World ◽  
Transaction Data ◽  
Retail Company ◽  
Special Strategy

The development of the business world is increasingly rapid, so it needs a special strategy to increase the turnover of the company, in this case the retail company. In increasing the company's turnover can be done using the Data Mining process, one of which is using apriori algorithm. With a priori algorithm can be found association rules which can later be used as patterns of purchasing goods by consumers, this study uses a repository of 209 records consisting of 23 transactions and 164 attributes. From the results of this study, the goods with the name CREAM CUPID HEART COAT HANGER are the products most often purchased by consumers. By knowing the pattern of purchasing goods by consumers, the company management can increase the company's turnover by referring to the results of processing sales transaction data using a priori algorithm

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