scholarly journals Analisa Karakteristik Kecelakaan Lalu Lintas di Jalan Ahmad Yani Surabaya melalui Pendekatan Knowledge Discovery in Database

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Arvian Zanuardi ◽  
Hitapriya Suprayitno
Keyword(s):  
Knowledge Discovery ◽  
Knowledge Discovery In Database

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

KDK BASED DOUBLE-BASIS FUSION MECHANISM AND ITS PROCESS MODEL

2005 ◽  
Vol 14 (03) ◽  
pp. 399-423 ◽  
Author(s):  
BINGRU YANG ◽  
JIANGTAO SHEN ◽  
WEI SONG
Keyword(s):  
Knowledge Base ◽  
Knowledge Discovery ◽  
Process Model ◽  
Discovery Process ◽  
New Horizons ◽  
Mining Algorithm ◽  
New Process ◽  
Knowledge Discovery In Database

Knowledge Discovery in Knowledge Base (KDK) opens new horizons for research. KDK and KDD (Knowledge Discovery in Database) are the different cognitive field and discovery process. In most people's view, they are independent each other. In this paper we can summarize the following tasks: Firstly, we discussed that two kinds of the process model and mining algorithm of KDK based on facts and rules in knowledge base. Secondly, we proves that the inherent relation between KDD and KDK (i.e. double-basis fusion mechanism). Thirdly, we gained the new process model and implementation technology of KDK*. Finally, the imitation experimentation proved that the validity of above mechanism and process model.

Managing talent in human resource : a Knowledge Discovery in Database (KDD) approach

2009 ◽  
Vol 6 (1) ◽  
pp. 51
Author(s):  
Hamidah Jantan ◽  
Abdul Razak Hamdan ◽  
Zulaiha Ali Othman
Keyword(s):  
Data Analysis ◽  
Knowledge Discovery ◽  
Human Resource ◽  
Talent Management ◽  
Past Experience ◽  
Analysis Approach ◽  
Development Finance ◽  
The Past ◽  
Knowledge Discovery In Database ◽  
The Right

In any organization, managing human talent is very important and need more attentions from Human Resource (HR) professionals. Nowadays, among the challenges of HR professionals is to manage an organization’s talent, especially to ensure the right person is assigned to the right job at the right time. Knowledge Discovery in Database (KDD) is a data analysis approach that is commonly used for classification and prediction; and this approach has been widely used in many fields such as manufacturing, development, finance and etc. However, this approach has not attracted people in human resource especially for talent management. For this reason, this paper presents an overview of some talent management problems that can be solved by using KDD approach. In this study, we attempt to implement one of the talent management tasks i.e. identifying potential talent by predicting their performance. The employee’s performance can be predicted based on the past experience knowledge which is discovered from existing databases. Finally, this paper proposes the suggested framework for talent management using KDD approach.

Actionable Knowledge Discovery

2011 ◽  
pp. 8-13
Author(s):  
Longbing Cao
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Social Intelligence ◽  
Pattern Mining ◽  
Real Life ◽  
Simulated Data ◽  
Business Environment ◽  
Actionable Knowledge ◽  
Real Life Data ◽  
Knowledge Discovery In Database

Actionable knowledge discovery is selected as one of the greatest challenges (Ankerst, 2002; Fayyad, Shapiro, & Uthurusamy, 2003) of next-generation knowledge discovery in database (KDD) studies (Han & Kamber, 2006). In the existing data mining, often mined patterns are nonactionable to real user needs. To enhance knowledge actionability, domain-related social intelligence is substantially essential (Cao et al., 2006b). The involvement of domain-related social intelligence into data mining leads to domaindriven data mining (Cao & Zhang, 2006a, 2007a), which complements traditional data-centered mining methodology. Domain-related social intelligence consists of intelligence of human, domain, environment, society and cyberspace, which complements data intelligence. The extension of KDD toward domain-driven data mining involves many challenging but promising research and development issues in KDD. Studies in regard to these issues may promote the paradigm shift of KDD from data-centered interesting pattern mining to domain-driven actionable knowledge discovery, and the deployment shift from simulated data set-based to real-life data and business environment-oriented as widely predicted.

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