A Hybrid Multilayer Perceptron Neural Network for Direct Marketing

Data Mining ◽  
2013 ◽  
pp. 1534-1544
Author(s):  
M. Govindarajan ◽  
RM. Chandrasekaran
Keyword(s):  
Data Mining ◽  
Model Selection ◽  
Multilayer Perceptron ◽  
Direct Marketing ◽  
Customer Relationship ◽  
Classification Problems ◽  
Validation Data ◽  
Data Set ◽  
Knowledge Discovery In Database ◽  
Learning Data

Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes feature selection and model selection simultaneously for Multilayer Perceptron (MLP) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the classifier significantly. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: Direct Marketing in Customer Relationship Management. It is shown that, compared to earlier MLP technique, the run time is reduced with respect to learning data and with validation data for the proposed Multilayer Perceptron (MLP) classifiers. Similarly, the error rate is relatively low with respect to learning data and with validation data in direct marketing dataset. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

A Hybrid Multilayer Perceptron Neural Network for Direct Marketing

2012 ◽  
Vol 2 (3) ◽  
pp. 63-73 ◽  
Author(s):  
M. Govindarajan ◽  
RM. Chandrasekaran
Keyword(s):  
Data Mining ◽  
Model Selection ◽  
Multilayer Perceptron ◽  
Direct Marketing ◽  
Customer Relationship ◽  
Classification Problems ◽  
Validation Data ◽  
Data Set ◽  
Knowledge Discovery In Database ◽  
Learning Data

Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes feature selection and model selection simultaneously for Multilayer Perceptron (MLP) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the classifier significantly. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: Direct Marketing in Customer Relationship Management. It is shown that, compared to earlier MLP technique, the run time is reduced with respect to learning data and with validation data for the proposed Multilayer Perceptron (MLP) classifiers. Similarly, the error rate is relatively low with respect to learning data and with validation data in direct marketing dataset. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

A Hybrid Multilayer Perceptron Neural Network for Direct Marketing

2012 ◽  
pp. 1228-1238
Author(s):  
M. Govindarajan ◽  
RM. Chandrasekaran
Keyword(s):  
Data Mining ◽  
Model Selection ◽  
Multilayer Perceptron ◽  
Direct Marketing ◽  
Customer Relationship ◽  
Classification Problems ◽  
Validation Data ◽  
Data Set ◽  
Knowledge Discovery In Database ◽  
Learning Data

Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes feature selection and model selection simultaneously for Multilayer Perceptron (MLP) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the classifier significantly. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: Direct Marketing in Customer Relationship Management. It is shown that, compared to earlier MLP technique, the run time is reduced with respect to learning data and with validation data for the proposed Multilayer Perceptron (MLP) classifiers. Similarly, the error rate is relatively low with respect to learning data and with validation data in direct marketing dataset. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

Interactive Classification Using a Granule Network

2011 ◽  
pp. 235-245
Author(s):  
Yan Zhao ◽  
Yiyu Yao
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Pattern Recognition ◽  
Classification System ◽  
Search Space ◽  
Classification Methods ◽  
Classification Problems ◽  
Computing Model ◽  
Tree Construction ◽  
Learning Data

Classification is one of the main tasks in machine learning, data mining, and pattern recognition. Compared with the extensively studied automation approaches, the interactive approaches, centered on human users, are less explored. This chapter studies interactive classification at 3 levels. At the philosophical level, the motivations and a process-based framework of interactive classification are proposed. At the technical level, a granular computing model is suggested for re-examining not only existing classification problems, but also interactive classification problems. At the application level, an interactive classification system (ICS), using a granule network as the search space, is introduced. ICS allows multi-strategies for granule tree construction, and enhances the understanding and interpretation of the classification process. Interactive classification is complementary to the existing classification methods.

Predictive Data Mining

2011 ◽  
pp. 3105-3110 ◽  
Author(s):  
Sotiris Kotsiantis ◽  
Panayotis Pintelas
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Computer Programs ◽  
Classification Problems ◽  
Data Set ◽  
Future Data ◽  
Regression Problems ◽  
Discrete Values ◽  
Predictive Data Mining ◽  
Technical Basis

Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. The idea is to build computer programs that sift through databases automatically, seeking regularities or patterns. Strong patterns, if found, will likely generalize to make accurate predictions on future data. Machine learning (ML) provides the technical basis of data mining. It is used to extract information from the raw data in databases—information that is expressed in a comprehensible form and can be used for a variety of purposes. Every instance in any data set used by ML algorithms is represented using the same set of features. The features may be continuous, categorical, or binary. If instances are given with known labels (the corresponding correct outputs), then the learning is called supervised in contrast to unsupervised learning, where instances are unlabeled (Kotsiantis & Pintelas, 2004). This work is concerned with regression problems in which the output of instances admits real values instead of discrete values in classification problems.

Decision Support System For A Customer Relationship Management Case Study

2014 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Özge Kart ◽  
Alp Kut ◽  
Vladimir Radevski
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Banking Sector ◽  
Relationship Management ◽  
Customer Relationship ◽  
Data Set ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Management Case Study

<span lang="EN-US">Data mining is a computational approach aiming to discover hidden and valuable information in large datasets. It has gained importance recently in the wide area of computational among which many in the domain of Business Informatics. This paper focuses on applications of data mining in Customer Relationship Management (CRM). The core of our application is a classifier based on the naive Bayesian classification. The accuracy rate of the model is determined by doing cross validation. The results demonstrated the applicability and effectiveness of the proposed model. Naive Bayesian classifier reported high accuracy. So the classification rules can be used to support decision making in CRM field. The aim of this study is to apply the data mining model to the banking sector as example case study. This work also contains an example data set related with customers to predict if the client will subscribe a term deposit. The results of the implementation are available on a mobile platform. </span>

scholarly journals Technical and Vocational Education Analytics Using Punjab TEVTA Students’ Data

2018 ◽  
pp. 37-48
Keyword(s):  
Data Mining ◽  
Vocational Education ◽  
Teaching And Learning ◽  
Teaching Method ◽  
Technical Education ◽  
Educational Data Mining ◽  
Vocational Education And Training ◽  
Classification Problems ◽  
Data Set ◽  
Data Mining Techniques

The development of informative workforce that is skilled in a specific profession is considered as the most recommended and desirable feature of any advanced state. Technical Education & Vocational Trainings provide golden opportunity of growth regarding the output of individuals and prosperity of employers. Subsequently it is the dire need of developing countries to invest in public vocational education and training sector (VET) for the progression of skillful societies. Process of manual predictions and analysis on the basis of students’ data to make decisions that will improve the overall teaching and learning is very difficult and tiring. Data mining is exceptionally helpful when we are talking about education data analysis and prediction. Data mining techniques are being used successfully in different areas especially in student educational and learning analytics called as Educational Data Mining (EDM). In this work, TEVTA students’ data is shaped as a ready-to-mine data set and then various data mining techniques are applied to derive interesting patterns that can potentially derive important decisions for improvement of learning process, enhancement of teaching method and overall development of whole system of technical education and vocational trainings. Besides presenting interesting analytics of TEVTA data, we develop classification problems to predict status of students after completing TEVTA courses. This classification can also help in evaluating success of TEVTA programs. This work can help in analyzing and predicting the aspects affecting students’ as well as institutes’ performance from different dimensions.

scholarly journals The implementation of Customer Relationship Management (CRM) on textile supply chain using k-means clustering in data mining

2018 ◽  
Vol 204 ◽  
pp. 04017 ◽  
Author(s):  
Anik Dwiastuti ◽  
Aisyah Larasati ◽  
Endang Prahastuti
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Customer Relationship Management ◽  
Textile Industry ◽  
Customer Value ◽  
Relationship Management ◽  
Customer Relationship ◽  
Clustering Methods ◽  
Data Set ◽  
Textile Supply Chain

Supply chain in textile industry requires an involvement of several other related industry therefore it divide into several sub-sector industry. The market dynamic and complexity of supply chain network are causing problem. This study aims to classify the market base on consumers behaviour through their preferences in textile product in East Java. Analysis of data using data mining approach. Algorithm K-means type clustering is use as clustering methods by integrating with Customer Relationship Management (CRM) concept. The simulation result of data set using five cluster depends on their variability value are Lumajang, Malang, Madura, Tulungagung, and Ponorogo. The clusters formed have the highest importance predictor in “way of purchase” and the lowest in “purchase flexibility”. The result in this study is generally indicate that consumers of textile products in East Java prioritize values in customer value compared to product quality.

The Issue of Missing Values in Data Mining

2011 ◽  
pp. 1102-1109
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Data Mining ◽  
Missing Data ◽  
Incomplete Data ◽  
Missing Values ◽  
Large Data ◽  
Original Data ◽  
Customer Relationship ◽  
Data Sets ◽  
Data Mining Technique ◽  
Data Set

The essence of data mining is to investigate for pertinent information that may exist in data (often large data sets). The immeasurably large amount of data present in the world, due to the increasing capacity of storage media, manifests the issue of the presence of missing values (Olinsky et al., 2003; Brown and Kros, 2003). The presented encyclopaedia article considers the general issue of the presence of missing values when data mining, and demonstrates the effect of when managing their presence is or is not undertaken, through the utilisation of a data mining technique. The issue of missing values was first exposited over forty years ago in Afifi and Elashoff (1966). Since then it is continually the focus of study and explanation (El-Masri and Fox-Wasylyshyn, 2005), covering issues such as the nature of their presence and management (Allison, 2000). With this in mind, the naïve consistent aspect of the missing value debate is the limited general strategies available for their management, the main two being either the simple deletion of cases with missing data or a form of imputation of the missing values in someway (see Elliott and Hawthorne, 2005). Examples of the specific investigation of missing data (and data quality), include in; data warehousing (Ma et al., 2000), and customer relationship management (Berry and Linoff, 2000). An alternative strategy considered is the retention of the missing values, and their subsequent ‘ignorance’ contribution in any data mining undertaken on the associated original incomplete data set. A consequence of this retention is that full interpretability can be placed on the results found from the original incomplete data set. This strategy can be followed when using the nascent CaRBS technique for object classification (Beynon, 2005a, 2005b). CaRBS analyses are presented here to illustrate that data mining can manage the presence of missing values in a much more effective manner than the more inhibitory traditional strategies. An example data set is considered, with a noticeable level of missing values present in the original data set. A critical increase in the number of missing values present in the data set further illustrates the benefit from ‘intelligent’ data mining (in this case using CaRBS).

Analisis Data Pembayaran Kredit Nasabah Bank Menggunakan Metode Data Mining

2013 ◽  
Vol 4 (1) ◽  
pp. 18-27
Author(s):  
Ira Melissa ◽  
Raymond S. Oetama
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Database

Data mining adalah analisis atau pengamatan terhadap kumpulan data yang besar dengan tujuan untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara yang lebih mudah dimengerti dan bermanfaat bagi pemilik data. Data mining merupakan proses inti dalam Knowledge Discovery in Database (KDD). Metode data mining digunakan untuk menganalisis data pembayaran kredit peminjam pembayaran kredit. Berdasarkan pola pembayaran kredit peminjam yang dihasilkan, dapat dilihat parameter-parameter kredit yang memiliki keterkaitan dan paling berpengaruh terhadap pembayaran angsuran kredit. Kata kunci—data mining, outlier, multikolonieritas, Anova

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