Post-processing Data Mining Models for Actionability

2008 ◽  
pp. 11-30 ◽  
Author(s):  
Qiang Yang
Keyword(s):  
Data Mining ◽  
Post Processing ◽  
Processing Data

Semantic Analytical Reports: A Framework for Post-processing Data Mining Results

2009 ◽  
pp. 88-98 ◽  
Author(s):  
Tomáš Kliegr ◽  
Martin Ralbovský ◽  
Vojtěch Svátek ◽  
Milan Šimůnek ◽  
Vojtěch Jirkovský ◽  
...  
Keyword(s):  
Data Mining ◽  
Post Processing ◽  
Processing Data

Data mining experiments on hop processing data

2005 ◽  
Author(s):  
J.W. Grzymala-Busse ◽  
Z.S. Hippe ◽  
T. Mroczek ◽  
E. Roj ◽  
B. Skowronski
Keyword(s):  
Data Mining ◽  
Processing Data

Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ

2020 ◽  
Vol 12 (2) ◽  
pp. 104-107
Author(s):  
Nurhayati . ◽  
Nuraeny Septianti ◽  
Nani Retnowati ◽  
Arief Wibowo
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Processing ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Processing Data ◽  
Student Graduation ◽  
Phase Data ◽  
Bayes Algorithm

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%.

Data Mining Applications in the Hospitality Industry

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.

Graph-Based Modelling of Concurrent Sequential Patterns

2010 ◽  
Vol 6 (2) ◽  
pp. 41-58 ◽  
Author(s):  
Jing Lu ◽  
Weiru Chen ◽  
Malcolm Keech
Keyword(s):  
Data Mining ◽  
Graph Representation ◽  
Sequential Patterns ◽  
Mining Method ◽  
Post Processing ◽  
Synthetic Sample ◽  
Novel Approach ◽  
Sample Data ◽  
Modelling Methods ◽  
Graph Based Model

Structural relation patterns have been introduced recently to extend the search for complex patterns often hidden behind large sequences of data. This has motivated a novel approach to sequential patterns post-processing and a corresponding data mining method was proposed for Concurrent Sequential Patterns (ConSP). This article refines the approach in the context of ConSP modelling, where a companion graph-based model is devised as an extension of previous work. Two new modelling methods are presented here together with a construction algorithm, to complete the transformation of concurrent sequential patterns to a ConSP-Graph representation. Customer orders data is used to demonstrate the effectiveness of ConSP mining while synthetic sample data highlights the strength of the modelling technique, illuminating the theories developed.

Post-Processing for Rule Reduction Using Closed Set

2009 ◽  
pp. 81-99 ◽  
Author(s):  
Huawen Liu ◽  
Jigui Sun ◽  
Huijie Zhang
Keyword(s):  
Data Mining ◽  
Efficient Method ◽  
End Users ◽  
Rule Base ◽  
Main Character ◽  
Post Processing ◽  
Closed Set ◽  
Rule Management ◽  
Large Databases ◽  
Dependent Relation

In data mining, rule management is getting more and more important. Usually, a large number of rules will be induced from large databases in many fields, especially when they are dense. This, however, directly leads to the gained knowledge hard to be understood and interpreted. To eliminate redundant rules from rule base, many efforts have been made and various efficient and outstanding algorithms have been proposed. However, end-users are often unable to complete a mining task because there are still insignificant rules. Thus, it becomes apparent that an efficient technique is needed to discard useless rules as more as possible, without information lossless. To achieve this goal, in this paper we propose an efficient method to filter superfluous rules from knowledge base in a post-processing manner. The main character of our method lies in that it eliminates redundancy of rules by dependent relation, which can be discovered by closed set mining technique. Their performance evaluations show that the compression degree achieved by our proposed method is better and its efficiency is also higher than those of other techniques.

Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules

2009 ◽  
pp. 38-55 ◽  
Author(s):  
Solange Oliveira Rezende ◽  
Edson Augusto Melanda ◽  
Magaly Lika Fujimoto ◽  
Roberta Akemi Sinoara ◽  
Veronica Oliveira de Carvalho
Keyword(s):  
Data Mining ◽  
Data Driven ◽  
Objective Measures ◽  
Post Processing ◽  
Rule Mining ◽  
Evaluation Measures ◽  
Combining Data ◽  
Pros And Cons ◽  
The Subject

Association rule mining is a data mining task that is applied in several real problems. However, due to the huge number of association rules that can be generated, the knowledge post-processing phase becomes very complex and challenging. There are several evaluation measures that can be used in this phase to assist users in finding interesting rules. These measures, which can be divided into data-driven (or objective measures) and user-driven (or subjective measures), are first discussed and then analyzed for their pros and cons. A new methodology that combines them, aiming to use the advantages of each kind of measure and to make user’s participation easier, is presented. In this way, data-driven measures can be used to select some potentially interesting rules for the user’s evaluation. These rules and the knowledge obtained during the evaluation can be used to calculate user-driven measures, which are used to aid the user in identifying interesting rules. In order to identify interesting rules that use our methodology, an approach is described, as well as an exploratory environment and a case study to show that the proposed methodology is feasible. Interesting results were obtained. In the end of the chapter tendencies related to the subject are discussed.

Data Mining Utilization for Plant Historical Processing Accumulated Data

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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