The Application of Data Mining for Direct Marketing

Patil, Purushottam R.; Revankar, Pravin; Joshi, Prashant D.

doi:10.1109/icetet.2009.221

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…They are predictive models and they are used in computing for calculating probabilities. The data usually play an important role in determining the quality of the decision trees [13].…”

Section: The Decision Treementioning

confidence: 99%

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Processing GIS Data Using Decision Trees and an Inductive Learning Method

Mihai¹,

Mocanu²

2021

IJMLC

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This paper extends recent work on spatial data mining, with another application of the classification techniques, namely with the Decision tree classifier algorithm. Spatial data mining represents a various and investigated domain because huge amounts of spatial data have been collected, ranging from remote sensing to geographical information system and computer cartography. In this work we used the Weka tool to implement the C4.5 (Quinlan) Decision tree algorithm on a dataset of Geographic Information System (GIS), data collection called Cadastre formed by a parcel plan from the Dolj district of Romania. The results of the experiments highlight several advantages and also some disadvantages of Decision tree in context of spatial data mining, with a favorable accuracy.

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“…They are predictive models and they are used in computing for calculating probabilities. The data usually play an important role in determining the quality of the decision trees [13].…”

Section: The Decision Treementioning

confidence: 99%

“…), where the x j represents attribute values or features of the sample. The training data is augmented with a vector A = a 1 , a 2 , .... where a 1 , a 2 , .... represent the class to which sample belongs [13].…”

Section: The Decision Treementioning

confidence: 99%

Processing GIS Data Using Decision Trees and an Inductive Learning Method

Mihai¹,

Mocanu²

2021

IJMLC

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“…Bank merupakan perusahaan yang memiliki data yang besar yang tersimpan di dalam database dan diolah menghasilkan sebuah informasi yang saling berkaitan tentang nasabah, data tersebut serta dapat digunakan untuk menjaga hubungan antar bank dengan nasabah yang valid, sehingga berguna untuk menentukan secara individual tentang penawaran produk bank [1].…”

Section: Pendahuluanunclassified

Perbandingan Metode Klasifikasi Data Mining untuk Nasabah Bank Telemarketing

Subarkah¹,

Pambudi²,

Hidayah³

2020

matrik

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Bank merupakan perusahaan yang memiliki data yang besar yang tersimpan di dalam database dan diolah menghasilkan sebuah informasi yang saling berkaitan tentang nasabah. Bank, harus memiliki ide dan terobosan baru guna mengetahui kendala pada nasabah telemarketing yang ingin melakukan deposito pada Bank tersebut, agar Bank terhindar dari ancaman krisis keuangan. Penelitian ini menguji keberhasilan Bank telemarketing dengan cara melakukan klasifikasi keputusan nasabah dengan menerapkan data mining. Metode yang di gunakan algoritma Classification and Regression Trees (CART) dan naive bayes menggunakan dataset diambil dari University of California Irvine (UCI) Repository Learning. Adapun metode validasi dan evaluasi yang digunakan yaitu 10-cross validation dan confusion matrix. Hasil akurasi pada algoritma CART yaitu 89.51% dengan nilai precision 87%, Recall 89% dan F-Measure 88% dan pada algoritma naive bayes mendapatkan nilai akurasi sebesar 86.88% dengan nilai precision 87%, Recall 86% dan F-Measure 87%. Dari hasil tersebut dapat disimpulkan bahwa algoritma CART lebih baik dalam memprediksi keputusan nasabah telemarketing tepat dalam penawaran deposito.

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