Overview of Knowledge Discovery and Data Mining Process Models

2015 ◽  
pp. 11-24
Author(s):  
Sumana Sharma
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Process Models

Toward an integrated knowledge discovery and data mining process model

2010 ◽  
Vol 25 (1) ◽  
pp. 49-67 ◽  
Author(s):  
Sumana Sharma ◽  
Kweku-Muata Osei-Bryson
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Process Model ◽  
Process Models ◽  
Data Preparation ◽  
Important Goal ◽  
Efficiency And Effectiveness ◽  
Integrated Knowledge

AbstractThe knowledge discovery and data mining (KDDM) process models describe the various phases (e.g. business understanding, data understanding, data preparation, modeling, evaluation and deployment) of the KDDM process. They act as a roadmap for implementation of the KDDM process by presenting a list of tasks for executing the various phases. The checklist approach of describing the tasks is not adequately supported by appropriate tools, which specify ‘how’ the particular task can be implemented. This may result in tasks not being implemented. Another disadvantage is that the long checklist does not capture or leverage the dependencies that exist among the various tasks of the same and different phases. This not only makes the process cumbersome to implement, but also hinders possibilities for semi-automation of certain tasks. Given that each task in the process model serves an important goal and even affects the execution of related tasks due to the dependencies, these limitations are likely to negatively affect the efficiency and effectiveness of KDDM projects. This paper proposes an improved KDDM process model that overcomes these shortcomings by prescribing tools for supporting each task as well as identifying and leveraging dependencies among tasks for semi-automation of tasks, wherever possible.

A survey of Knowledge Discovery and Data Mining process models

2006 ◽  
Vol 21 (1) ◽  
pp. 1-24 ◽  
Author(s):  
LUKASZ A. KURGAN ◽  
PETR MUSILEK
Keyword(s):  
Data Mining ◽  
Research And Development ◽  
Knowledge Discovery ◽  
Process Model ◽  
Process Models ◽  
Historical Overview ◽  
Future Directions ◽  
Comprehensive Comparison ◽  
Development Area ◽  
Current Standards

Knowledge Discovery and Data Mining is a very dynamic research and development area that is reaching maturity. As such, it requires stable and well-defined foundations, which are well understood and popularized throughout the community. This survey presents a historical overview, description and future directions concerning a standard for a Knowledge Discovery and Data Mining process model. It presents a motivation for use and a comprehensive comparison of several leading process models, and discusses their applications to both academic and industrial problems. The main goal of this review is the consolidation of the research in this area. The survey also proposes to enhance existing models by embedding other current standards to enable automation and interoperability of the entire process.

A survey of data mining and knowledge discovery process models and methodologies

2010 ◽  
Vol 25 (2) ◽  
pp. 137-166 ◽  
Author(s):  
Gonzalo Mariscal ◽  
Óscar Marbán ◽  
Covadonga Fernández
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Scientific Literature ◽  
State Of The Art ◽  
Process Models ◽  
Knowledge Discovery In Databases ◽  
Discovery Process ◽  
Advantages And Disadvantages ◽  
Discovery Process Models ◽  
Discovery Project

AbstractUp to now, many data mining and knowledge discovery methodologies and process models have been developed, with varying degrees of success. In this paper, we describe the most used (in industrial and academic projects) and cited (in scientific literature) data mining and knowledge discovery methodologies and process models, providing an overview of its evolution along data mining and knowledge discovery history and setting down the state of the art in this topic. For every approach, we have provided a brief description of the proposed knowledge discovery in databases (KDD) process, discussing about special features, outstanding advantages and disadvantages of every approach. Apart from that, a global comparative of all presented data mining approaches is provided, focusing on the different steps and tasks in which every approach interprets the whole KDD process. As a result of the comparison, we propose a new data mining and knowledge discovery process namedrefined data mining processfor developing any kind of data mining and knowledge discovery project. The refined data mining process is built on specific steps taken from analyzed approaches.

scholarly journals DATA MINING PROCESS MODELS: A ROADMAP FOR KNOWLEDGE DISCOVERY

2012 ◽  
pp. 405-433
Author(s):  
Armando B. Mendes ◽  
Luís Cavique ◽  
Jorge M.A. Santos
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Process Models

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

The AI Delusion

2018 ◽  
Author(s):  
Gary Smith
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Industrial Revolution ◽  
The Real ◽  
Intelligent Machines ◽  
Black Boxes ◽  
Real Danger ◽  
The Way

We live in an incredible period in history. The Computer Revolution may be even more life-changing than the Industrial Revolution. We can do things with computers that could never be done before, and computers can do things for us that could never be done before. But our love of computers should not cloud our thinking about their limitations. We are told that computers are smarter than humans and that data mining can identify previously unknown truths, or make discoveries that will revolutionize our lives. Our lives may well be changed, but not necessarily for the better. Computers are very good at discovering patterns, but are useless in judging whether the unearthed patterns are sensible because computers do not think the way humans think. We fear that super-intelligent machines will decide to protect themselves by enslaving or eliminating humans. But the real danger is not that computers are smarter than us, but that we think computers are smarter than us and, so, trust computers to make important decisions for us. The AI Delusion explains why we should not be intimidated into thinking that computers are infallible, that data-mining is knowledge discovery, and that black boxes should be trusted.

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