Data mining experiments on hop processing data

Data processing is imperative for the development of information technology. Almost any field of work has information about data. The data is made use of the analysis of the job. Nowadays, information data is imperatively processed to help workers in making decisions. This study discusses student prediction graduation rates by using the naïve Bayes method. That aims at providing information to college if they can use it properly to utilize the data of students who graduated by processing data mining. Based on the data mining process, steps founded that used producing information, namely predicting student graduation on time. The method of this study is Naïve Bayes with classification techniques. At this study, researchers used a six-phase data mining process of industry crossing standards in data mining known as CRISP-DM. The results of research concluded that the application of the Naive Bayes algorithm uses 4 (four) parameters namely ips, ipk, the number of credits, and graduation by getting an accuracy value of 80.95%.

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Data Mining Applications in the Hospitality Industry

Business Information Systems ◽

10.4018/978-1-61520-969-9.ch067 ◽

2010 ◽

pp. 1109-1114

Author(s):

Soo Kim

Keyword(s):

Data Mining ◽

Customer Value ◽

Hospitality Industry ◽

Marketing Strategies ◽

Restaurant Industry ◽

Data Mining Techniques ◽

Competitive Edge ◽

Processing Data ◽

Hospitality Managers

Some people say that “success or failure often depends not only on how well you are able to collect data but also on how well you are able to convert them into knowledge that will help you better manage your business (Wilson, 2001, p. 26).” It is said the $391 billion restaurant industry generates a massive amount of data at each purchase (Wilson, 2001), and once collected, such collected data could be a gigantic tool for profits. In the hospitality industry, knowing your guests in terms of where they are from, how much they spend money, and when and what they spend it can help hospitality managers formulate marketing strategies, enhance guest experiences, increase retention and loyalty and ultimately, maximize profits. Data mining techniques are suitable for profiling hotel and restaurant customers due to their proven ability to create customer value (Magnini, Honeycutt, & Hodge, 2003; Min, Min & Emam, 2002). Furthermore, if the hospitality industry uses such data mining processes as collecting, storing, and processing data, the industry can get strategic competitive edge (Griffin, 1998). Unfortunately, however, the hospitality industry and managers are behind of using such data mining strategies, compared to the retail and grocery industries (Bogardus, 2001; Dev & Olsen, 2000). Therefore, there is a need for learning about such data mining systems for the hospitality industry. The purpose of this paper is to show the applications of data mining systems, to present some successes of the systems, and, in turn, to discuss some benefits from the systems in the hospitality industry.

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Data Mining Utilization for Plant Historical Processing Accumulated Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.1040 ◽

2013 ◽

Vol 411-414 ◽

pp. 1040-1043

Author(s):

Qing Li ◽

Bao Liang Ge ◽

Jie Liu ◽

Yan Xiong Fu

Keyword(s):

Data Mining ◽

Positive Relationship ◽

Copper Recovery ◽

Mining Technology ◽

Copper Concentrate ◽

Processing Data ◽

Total Metal ◽

Plant Processing ◽

Concentrate Grade ◽

Relationship Of

A large amount of processing data was accumulated within plant processing. And its necessary to use this data for plant processing as well as its administration. In this article the data mining technology and its utilization were discussed, according to research results, the fitting relationship is:Cu recovery (%)= -1.1221*lime dosage (Kg/t)+92.6, the lime dosage alteration effect on copper recovery are 1.41% absolutely and 1.66% relatively. The fitting relationship of copper concentrate grade and lime dosage is:Cu grade (%)= 0.0554*lime dosage (Kg/t)+19.271, the lime dosage alteration effect on copper recovery are 0.070% absolutely and 0.36% relatively. It can be concluded that the lime dosage has a great effect on copper recovery, and lime dosage is relative to the total metal minerals in the ore, because the fitting relationship of the lime dosage and metal minerals summation in the ore is:Dosage (Kg/t)= 0.0487*total metal minerals (%)+2.6441, the lime dosage show a positive relationship with total metal minerals in the ore.

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Dynamic Euclidean K-Means Clustering Algorithm in Data Mining

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9443.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 11129-11133

Keyword(s):

Data Mining ◽

Euclidean Distance ◽

Clustering Algorithm ◽

Threshold Value ◽

Distance Metrics ◽

Processing Data ◽

Data Grouping ◽

Logical Information ◽

Analyze Data ◽

Squared Euclidean Distance

Data mining was the practice of processing data in order to derive interesting patterns as well as designs from the system used to analyze data. Grouping was the process of grouping artifacts even though that items in almost the same category are more identical than items in other classes. The existing system main drawbacks are not able to show clear logical information about the market analysis and cannot summarize the strength, weakness, opportunities and threats. Among these clustering is considered as a significant technique to capture the structure of data. Data mining adds to clustering is complicated to retrieve Wide databases with either a variety of different forms of attributes. This includes special specific clustering strategies with Euclidean K-Means grouping process. The power of k-means algorithm is due to its computational efficiency and the nature of ease at which it can be used. In this technique the threshold value is used to determine the information is the same category or even a new team is formed. Proposing an Euclidean K-means algorithm is a necessity. The squared Euclidean distance metric results of the suggested algorithm are tested in this journal experimental results. Distance metrics are used to build reliable features and functionality including grouping for data mining. The simulation process is carried out in MATLAB tool and outperforms the proposed results.

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2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)

10.1109/dipdmwc37531.2016 ◽

2016 ◽

Keyword(s):

Data Mining ◽

Information Processing ◽

Wireless Communications ◽

Digital Information ◽

International Conference ◽

Processing Data ◽

Digital Information Processing

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Penggunaan Association Rule Data Mining Untuk Menentukan Pola Lama Studi Mahasiswa F-MIPA UNSRAT

d'CARTESIAN ◽

10.35799/dc.3.1.2014.3777 ◽

2014 ◽

Vol 3 (1) ◽

pp. 1

Author(s):

M. Zainal Mahmudin ◽

Altien Rindengan ◽

Winsy Weku

Keyword(s):

Data Mining ◽

Association Rule ◽

Large Data ◽

Apriori Algorithm ◽

Data Mining Algorithms ◽

Adequate Information ◽

Processing Data ◽

Rule Method ◽

Mining Algorithms ◽

Confidence Value

Abstract The requirement of highest information sometimes is not balance with the provision of adequate information, so that the information must be re-excavated in large data. By using the technique of association rule we can obtain information from large data such as the college data. The purposes of this research is to determine the patterns of study from student in F-MIPA UNSRAT by using association rule method of data mining algorithms and to compare in the apriori method and a hash-based algorithms. The major’s student data of F-MIPA UNSRAT as a data were processed by association rule method of data mining with the apriori algorithm and a hash-based algorithm by using support and confidance at least 1 %. The results of processing data with apriori algorithms was same with the processing results of hash-based algorithms is as much as 49 combinations of 2-itemset. The pattern that formed between 7,5% of graduates from mathematics major that studied for more 5 years with confidence value is 38,5%. Keywords: Apriori algorithm, hash-based algorithm, association rule, data mining. Abstrak Kebutuhan informasi yang sangat tinggi terkadang tidak diimbangi dengan pemberian informasi yang memadai, sehingga informasi tersebut harus kembali digali dalam data yang besar. Dengan menggunakan teknik association rule kita dapat memperoleh informasi dari data yang besar seperti data yang ada di perguruan tinggi. Tujuan penelitian ini adalah menentukan pola lama studi mahasiswa F-MIPA UNSRAT dengan menggunakan metode association rule data mining serta membandingkan algoritma apriori dan algoritma hash-based. Data yang digunakan adalah data induk mahasiswa F-MIPA UNSRAT yang diolah menggunakan teknik association rule data mining dengan algoritma apriori dan algoritma hash-based dengan minimum support 1% dan minimum confidance 1%. Hasil pengolahan data dengan algoritma apriori sama dengan hasil pengolahan data dengan algoritma hash-based yaitu sebanyak 49 kombinasi 2-itemset. Pola yang terbentuk antara lain 7,5% lulusan yang berasal dari jurusan matematika menempuh studi selama lebih dari 5 tahun dengan nilai confidence 38,5%. Kata kunci : Association rule data mining, algoritma apriori, algoritma hash-based

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Post-processing Data Mining Models for Actionability

Data Mining for Business Applications ◽

10.1007/978-0-387-79420-4_2 ◽

2008 ◽

pp. 11-30 ◽

Cited By ~ 2

Author(s):

Qiang Yang

Keyword(s):

Data Mining ◽

Post Processing ◽

Processing Data

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The XML Data Mining Research Based on the Multi-Level Technology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.1875 ◽

2014 ◽

Vol 644-650 ◽

pp. 1875-1878

Author(s):

Su Yu Huang ◽

Ping Fang Hu

Keyword(s):

Data Mining ◽

Traditional Method ◽

Data Exchange ◽

Standard Form ◽

Relational Data ◽

Mining Method ◽

Xml Data ◽

Processing Data ◽

Data Pretreatment ◽

Multi Level

XML has become the standard form of data exchange, more and more data in this form for storage, implying a lot of knowledge in these data information, the need for data mining processing. For XML data mining method at present, most of the need is to pass the XML data into relational data pretreatment process, using the traditional method for processing, data mining process is complex and the effect is not ideal. Therefore, there is an urgent need some effective methods for XML data mining directly.

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PENERAPAN DATA MINING UNTUK ANALISIS POLA BELANJA KONSUMEN MENGGUNAKAN ALGORITMA APRIORI PADA MALL CPM JAKARTA

Jurnal Sistem Informasi dan Informatika (Simika) ◽

10.47080/simika.v2i2.608 ◽

2019 ◽

Vol 2 (2) ◽

pp. 81-91

Author(s):

Persis Haryo Winasis

Keyword(s):

Data Mining ◽

A Priori ◽

Transaction Data ◽

Store Manager ◽

Processing Data ◽

Penerapan Teknik Clustering Data Mining untuk Memprediksi Kesesuaian Jurusan Siswa (Studi Kasus SMA PGRI 1 Subang)

Faktor Exacta ◽

10.30998/faktorexacta.v13i2.6588 ◽

2020 ◽

Vol 13 (2) ◽

pp. 125

Author(s):

Tubagus Riko Rivanthio ◽

Mardhiya Ramdhani

Keyword(s):

Data Mining ◽

Data Processing ◽

Private School ◽

Similar Data ◽

Clustering Techniques ◽

Academic Achievements ◽

Processing Data ◽

Using Data ◽

Clustering Data ◽

Selection Of

SMA PGRI 1 Subang is a private school that has several missions, one of which is the establishment of academic and non-academic achievements. In an effort to achieve the mission must supervise student achievement. The effort he did was to provide understanding in the selection of majors in accordance with the interests and talents of students. But in the activity of providing understanding, the school does not yet have a model that can evaluate the interests and talents of students to choose majors. The model can be obtained using student data processing. Data processing can be done using data mining, namely data mining clustering techniques. The technique will produce a model in the selection of majors. This clustering process is the process of grouping similar data based on the similarity of data held by students. The research method used is the CRISP-DM method which has 6 stages consisting of: Business Understanding, Data Understanding, Data Processing, Modeling, Evaluation, and Dissemination. The data that is processed is 620 data consisting of class of students in 2014, 2015, 2016. The results of processing using clustering obtained 6 clusters that have different models for each cluster. The results of this study can be used by schools in recommending courses chosen by students according to students' interests and talents, so students can learn optimally.Key words: clustering, dataMining, suitability, majors, students

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