I Shopping: Intelligent Shopping and Predicate Analysis System Using Data Mining

2015 ◽  
Author(s):  
Nethmi Deshani Hettiarachchi ◽  
Sobhani Umanga Pilapitiya ◽  
Nirmal Sankalpa Jayasinghe ◽  
Himash Deemantha ◽  
Sudheera Vitharana
Keyword(s):  
Data Mining ◽  
Using Data ◽  
Analysis System

The Study of Score Analysis for Secondary School Based on Data Mining Technology

2011 ◽  
Vol 219-220 ◽  
pp. 396-399
Author(s):  
Shang Fu Hao ◽  
Zhi Qiang Zhang ◽  
Ying Hui Wei
Keyword(s):  
Data Mining ◽  
Score Distribution ◽  
Mining Technology ◽  
Factors Affecting ◽  
Architecture Model ◽  
Scientific Teaching ◽  
Score Analysis ◽  
Secondary Teaching ◽  
Using Data ◽  
Analysis System

Nowadays, the contents associated with deep score analysis is rarely involved in the existing secondary teaching management software, which is not conductive to fully develop the information implied by these data,without scientific teaching evaluation. Using data mining technology, multiple aspects of student score distribution will be shown accurately, identifying the regular factors affecting score changes. Standard score as the mathematical model is adopted in the system, choosing the standard SOA architecture model, and a scientific and efficient score analysis system based on JAVA, JSP is developed. The system provides decision support information for academic departments to promote better teaching work, and finally improve the quality of teaching.

scholarly journals Data Mining technologies in managing the assortment of trading companies

2021 ◽  
Vol 16 (91) ◽  
pp. 99-109
Author(s):  
Lyudmila N. Loginova ◽  
◽  
Alexander M. Shash ◽  
Keyword(s):  
Data Mining ◽  
Customer Service ◽  
Sustainable Competitive Advantage ◽  
Joint Work ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Analysis System ◽  
The Stability ◽  
Fierce Competition ◽  
Mining Algorithms

In the conditions of fierce competition, satisfaction of all customer needs provides a trading enterprise with a sustainable competitive advantage. With the traditional structure of the assortment, there is a decrease in both the potential and real level of profit, the loss of competitive positions in promising markets, and, therefore, there is a decrease in the stability of the enterprise. The development of an analysis system to determine the specifics of the product range, optimize the range, and adapt it to the conditions of the Russian market is undoubtedly an urgent task. This article provides an overview of trade and IT companies that use data mining technologies. The survey showed that many companies are using data mining technology to improve customer service, turnover and sales in stores. In this regard, the management of Familia decided to develop its own software that will combine the analysis of turnover and sales in the company's stores in order to increase sales and improve the placement of goods in stores so that the client buys the necessary things, increasing the company's profit. The paper shows the possibility of combining several data mining methods in one system; shows the results of the analysis system and shows the effectiveness of the developed analysis system at Familia. The uniqueness of the developed software is the combination of data mining algorithms into one software product. The developed analysis system, based on the joint work of two data mining algorithms K-means and Apriori, allows you to manage the range of trade enterprises, reducing company losses.

scholarly journals Development of International Agricultural Trade Using Data Mining Algorithms-Based Trade Equality

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liu Yan
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Agricultural Trade ◽  
Machine Learning Techniques ◽  
Trade Data ◽  
Data Mining Algorithms ◽  
Efficient Data ◽  
Using Data ◽  
Analysis System ◽  
Data Analysis System

The development of international agriculture trade during the COVID-19 pandemic has encountered significant challenges. The processing of international agricultural trade data using machine learning techniques needs to be improved to perform effective analysis of agricultural trade. An essential issue for international agricultural trade is the accurate yield estimation for the numerous crops involved in international trade. Data mining techniques are the necessary approach for accomplishing practical and effective solutions for this problem. This paper combined the bidirectional encoder representations from transformers (BERT) model to conduct data mining and developed a trade data analysis system with efficient data analysis capabilities. Our results indicate that our model does reasonably well and obtains adequate information in deciding international agricultural trade. It can also be instrumental for policy and decision-making regarding international agricultural trade.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

Forecasting Automobile Retail Sales Using Data Mining The Case of Ranchi, Jharkhand, India

2018 ◽  
Vol 6 (9) ◽  
pp. 572-574
Author(s):  
Gyaneshwar Mahto ◽  
Umesh Prasad ◽  
Rajiv Kumar Dwivedi
Keyword(s):  
Data Mining ◽  
Retail Sales ◽  
Using Data
Sign in / Sign up

Export Citation Format

Share Document