scholarly journals Book Review

2021 ◽  
Vol 15 (1) ◽  
pp. 5-8
Author(s):  
Jhimli Adhikari
Keyword(s):  
Data Mining ◽  
Point Of View ◽  
Current Status ◽  
Domestic Sphere ◽  
Unique Problem ◽  
Embedded Knowledge ◽  
Using Data ◽  
New Research ◽  
Ict Market

This is a review of a book that discusses knowledge discovery using data mining and knowledge embedding through models. A number of scheme are reported in the book to explain how data mining and discovered embedded knowledge can be beneficial to social organizations, domestic sphere and ICT market. Each chapter of the book presents a unique problem of data mining philosophies from an embedded point of view. It will help researchers to understand the current status of big data mining and embedded knowledge, discover new research opportunities and gain more information about this field.

A Data Mining Approach to Forming Generic Bills of Materials in Support of Variant Design Activities

2004 ◽  
Vol 4 (4) ◽  
pp. 316-328 ◽  
Author(s):  
Carol J. Romanowski , ◽  
Rakesh Nagi
Keyword(s):  
Data Mining ◽  
Product Family ◽  
Research Direction ◽  
Design Constraints ◽  
Variant Design ◽  
Data Mining Approach ◽  
Design Activities ◽  
Using Data ◽  
New Research

In variant design, the proliferation of bills of materials makes it difficult for designers to find previous designs that would aid in completing a new design task. This research presents a novel, data mining approach to forming generic bills of materials (GBOMs), entities that represent the different variants in a product family and facilitate the search for similar designs and configuration of new variants. The technical difficulties include: (i) developing families or categories for products, assemblies, and component parts; (ii) generalizing purchased parts and quantifying their similarity; (iii) performing tree union; and (iv) establishing design constraints. These challenges are met through data mining methods such as text and tree mining, a new tree union procedure, and embodying the GBOM and design constraints in constrained XML. The paper concludes with a case study, using data from a manufacturer of nurse call devices, and identifies a new research direction for data mining motivated by the domains of engineering design and information.

Analysis of Climate Prediction and Climate Change in Pakistan Using Data Mining Techniques

2020 ◽  
pp. 321-338
Author(s):  
Soobia Saeed ◽  
N. Z. Jhanjhi ◽  
Mehmood Naqvi ◽  
Vasaki Ponnusamy ◽  
Mamoona Humayun
Keyword(s):  
Climate Change ◽  
Data Mining ◽  
Weather Forecasting ◽  
Point Of View ◽  
Weather Data ◽  
Maximum Temperature ◽  
Sufficient Information ◽  
Climate And Environmental Change ◽  
Environment Variables ◽  
Using Data

Weather forecasting is a significant meteorological task and has arisen in the last century from a rational and revolutionary point of view among the most difficult problems. The authors are researching the use of information mining techniques in this survey to measure maximum temperature, precipitation, dissipation, and wind speed. This was done using vector help profiles, decision tree, and weather data obtained in Pakistan in 2015 and 2019. For the planning of workbook accounts, an information system for meteorological information was used. The presentations of these calculations considered using standard implementing steps as well as the estimate that gave the best results for generating disposal rules for intermediate environment variables. Likewise, a prophetic network model for the climate outlook program, contradictory results, and true climate information for the projected periods have been created. The results show that with sufficient information on cases, data mining strategies can be used to estimate the climate and environmental change that it focuses on.

A Review on Applied Data Mining Techniques to Stock Market Prediction

2013 ◽  
pp. 108-126
Author(s):  
Neslihan Fidan ◽  
Beyza Ahlatcioglu Ozkok
Keyword(s):  
Data Mining ◽  
Stock Market ◽  
New Technologies ◽  
Point Of View ◽  
Stock Market Index ◽  
Data Mining Techniques ◽  
Market Index ◽  
Predictive Data Mining ◽  
Using Data ◽  
Analysis Point

A portfolio manager considers forecasting the asset prices and measurement of the market risk of an underlying asset. Financial institutions produce datasets to handle their problems by using data mining tools. Recently new technologies have been developed for tracking, collecting, and processing financial data. From a data analysis point of view, this chapter reviews the published articles based upon predictive data mining applications to stock market index. It is observed that hybrid models that combine data mining techniques or integrate an algorithm to a method work efficiently. Finally, the chapter provides likely directions of future researches.

scholarly journals An Analysis of the Determinants of Youth Employment in Rwanda

2018 ◽  
Vol 2 (2) ◽  
pp. 1-10
Author(s):  
Joseph Ndagijimana ◽  
Tharcisse Nzasingizimana ◽  
Almas Heshmati
Keyword(s):  
Poverty Reduction ◽  
Geographical Location ◽  
Youth Unemployment ◽  
Multinomial Logit Model ◽  
School To Work ◽  
Point Of View ◽  
Current Status ◽  
Youth Employment ◽  
Policy Recommendations ◽  
Using Data

The main objective of this research is to analyze the determinants of youth employment in Rwanda from the point of view of the demand, supply and the general labor market. An analysis of the data shows that a skill gap is most critical for employment creation and a transition from school-to-work seems problematic. Further, questions remain about what factors influence youth employment in Rwanda and how youth employment is related to poverty reduction and distribution of income. The study uses a multinomial logit model to shed light on the determinants of youth employment status in the country using data from the National Institute of Statistics of Rwanda (NISR). It verifies how the current status of youth employment in Rwanda has evolved over time and based on its findings it provides policy recommendations to promote youth employment. The research finds that youth employment in Rwanda is influenced by gender, age, education and geographical location. The finding of this research has implications for the youth unemployment in Kurdistan Region.

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%.

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