Software Projects Success Factors Identification using Data Mining

The paper presents the Critical success factors for implementing the Customer Relationship Management (CRM) in a firm using the Data mining (DM). The use of the data mining in CRM is widely accepted by the firms. The success of proper implementation of CRM using Data mining in firms is mixed. This is due to the fact that investment involve in this implementation requires planning regarding the factors which need to be considered before going for the new innovative technology. These factors may vary from firm to firm but the general factor for effective implementation of the CRM using data mining is essential. This factor termed as Critical success factor (CSF) decides the failure or success of the implementation. The paper demonstrates the key factors which need to be considered before automating the process of searching the mountain of customer’s related data using Data mining to find patterns that are good predictors of behaviors of the customer which help achieve successful CRM. The paper gives an idea of how proper planning and effective management can lead to increased customer satisfaction and profit for the firms.

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Extract hidden patterns in students' academic information to improve the curriculum by using data mining

International Review of Applied Sciences and Engineering ◽

10.1556/1848.2021.00269 ◽

2021 ◽

Author(s):

Saeide Amerioon ◽

Mohammad Mehdi Hosseini ◽

Mahshid Moradi

Keyword(s):

Data Mining ◽

Academic Success ◽

Success Factors ◽

Radial Basis Function Network ◽

Educational Data Mining ◽

Educational Background ◽

Data Mining Approach ◽

Academic Information ◽

Using Data ◽

Research Students

AbstractEducational data mining is an emerging exquisite field that has been successfully implemented in higher education. One of the best ways to increase the efficiency of this emerging phenomenon is to select efficient professors and effective teaching methods. This study is intended to show academic success factors to have better management in student curriculum, contextualizing the progress and to prevent unfavorable conditions for students. In this research, students of Shahrood University of Technology were studied. Initially, 3,765 samples of students' educational background were considered. Then, pre-processing was performed to make the data normalized. Next, several predictive models were developed using a supervised data mining approach. Next, five algorithms by the best result were selected. Comparing the results of algorithms applied to data, the two algorithms, radial basis function network and the Naïve Bayes with respectively value F-measure 0.929 and 0.942 showed more accurate results. Finally, the most effective feature was selected, the attributes ‘maximum semester’ and ‘the total number of units dropped’ were ranked an the most important, and the three attributes of ‘the basic courses average’, ‘the number of units of main courses’ and ‘the total average’, were placed in the next ranks.

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A Study on Detection of Small Size Malicious Code using Data Mining Method

Jouranl of Information and Security ◽

10.33778/kcsa.2019.19.1.011 ◽

2019 ◽

Vol 19 (1) ◽

pp. 11-17

Author(s):

Taek-Hyun Lee ◽

◽

Ho Kook Kwang

Keyword(s):

Data Mining ◽

Malicious Code ◽

Mining Method ◽

Data Mining Method ◽

Using Data

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A Study on the Analysis of Employment Decision Factor of the Visually Impaired using Data Mining Technique

Disability & Employment ◽

10.15707/disem.2013.23.1.011 ◽

2013 ◽

Vol 23 (1) ◽

pp. 273-302 ◽

Cited By ~ 6

Author(s):

임은정 ◽

신현욱 ◽

김성진

Keyword(s):

Data Mining ◽

Visually Impaired ◽

Data Mining Technique ◽

Mining Technique ◽

Employment Decision ◽

Using Data

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

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i11.468 ◽

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.

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

Creative Information Technology Journal ◽

10.24076/citec.2014v1i4.27 ◽

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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