Building Data Mining Models in the Oracle 9i Environment

10.28945/2697 ◽  
2003 ◽  
Author(s):  
Krzysztof Hauke ◽  
Mievzyslaw L. Owoc ◽  
Maciej Pondel
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Business Intelligence ◽  
Knowledge Bases ◽  
Crucial Issue ◽  
Knowledge Based ◽  
Whole Process ◽  
Database Server ◽  
Testing Data ◽  
Verification Techniques

Data Mining (DM) is a very crucial issue in knowledge discovery processes. The basic facilities to create data mining models were implemented successfully on Oracle 9i as the extension of the database server. DM tools enable developers to create Business Intelligence (BI) applications. As a result Data Mining models can be used as support of knowledge-based management. The main goal of the paper is to present new features of the Oracle platform in building and testing DM models. Authors characterize methods of building and testing Data Mining models available on the Oracle 9i platform, stressing the critical steps of the whole process and presenting examples of practical usage of DM models. Verification techniques of the generated knowledge bases are discussed in the mentioned environment.

Expression and Processing of Inductive Queries

2009 ◽  
pp. 518-526
Author(s):  
Edgard Benítez-Guerrero ◽  
Omar Nieva-García
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Pattern Discovery ◽  
Data Preprocessing ◽  
Knowledge Discovery In Databases ◽  
Digital Information ◽  
Interactive Process ◽  
Useful Knowledge ◽  
Knowledge Based ◽  
Computer Based

The vast amounts of digital information stored in databases and other repositories represent a challenge for finding useful knowledge. Traditionalmethods for turning data into knowledge based on manual analysis reach their limits in this context, and for this reason, computer-based methods are needed. Knowledge Discovery in Databases (KDD) is the semi-automatic, nontrivial process of identifying valid, novel, potentially useful, and understandable knowledge (in the form of patterns) in data (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996). KDD is an iterative and interactive process with several steps: understanding the problem domain, data preprocessing, pattern discovery, and pattern evaluation and usage. For discovering patterns, Data Mining (DM) techniques are applied.

Text Mining in the Context of Business Intelligence

2005 ◽  
pp. 2793-2798 ◽  
Author(s):  
Hércules Antonio do Prado ◽  
José Palazzo Moreira de Oliveira ◽  
Edilson Ferneda ◽  
Leandro Krug Wives ◽  
Edilberto Magalhães Silva ◽  
...  
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Knowledge Discovery ◽  
Business Intelligence ◽  
External Environment ◽  
Organizational Processes ◽  
External Monitoring ◽  
New Applications ◽  
Textual Form ◽  
Organizational Problems

Information about the external environment and organizational processes are among the most worthwhile input for business intelligence (BI). Nowadays, companies have plenty of information in structured or textual forms, either from external monitoring or from the corporative systems. In the last years, the structured part of this information stock has been massively explored by means of data-mining (DM) techniques (Wang, 2003), generating models that enable the analysts to gain insights on the solutions for organizational problems. On the text-mining (TM) side, the rhythm of new applications development did not go so fast. In an informal poll carried out in 2002 (Kdnuggets), just 4% of the knowledge-discovery-from-databases (KDD) practitioners were applying TM techniques. This fact is as intriguing as surprising if one considers that 80% of all information available in an organization comes in textual form (Tan, 1999).

VISUALIZATION SUPPORT FOR USER-CENTERED MODEL SELECTION IN KNOWLEDGE DISCOVERY AND DATA MINING

2001 ◽  
Vol 10 (04) ◽  
pp. 691-713 ◽  
Author(s):  
TUBAO HO ◽  
TRONGDUNG NGUYEN ◽  
DUCDUNG NGUYEN ◽  
SAORI KAWASAKI
Keyword(s):  
Data Mining ◽  
Model Selection ◽  
Knowledge Discovery ◽  
Performance Metrics ◽  
Model Performance ◽  
Discovery Process ◽  
Whole Process ◽  
Discovery System ◽  
Competing Models ◽  
Visualization Techniques

The problem of model selection in knowledge discovery and data mining—the selection of appropriate discovered patterns/models or algorithms to achieve such patterns/models—is generally a difficult task for the user as it requires meta-knowledge on algorithms/models and model performance metrics. Viewing knowledge discovery as a human-centered process that requires an effective collaboration between the user and the discovery system, our work aims to make model selection in knowledge discovery easier and more effective. For such a collaboration, our solution is to give the user the ability to try easily various alternatives and to compare competing models quantitatively and qualitatively. The basic idea of our solution is to integrate data and knowledge visualization with the knowledge discovery process in order to the support the participation of the user. We introduce the knowledge discovery system D2MS in which several visualization techniques of data and knowledge are developed and integrated into the steps of the knowledge discovery process. The visualizers in D2MS greatly help the user gain better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge in the whole process.

Data Mining and Knowledge Discovery

2010 ◽  
pp. 84-110
Author(s):  
Zude Zhou ◽  
Huaiqing Wang ◽  
Ping Lou
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Intelligent Manufacturing ◽  
Support Vector ◽  
Knowledge Based ◽  
Database Technology ◽  
Multi Agent ◽  
Basic Functions ◽  
Visualization Technology ◽  
Clustering Data

In Chapters 2 and 3, the knowledge-based system and Multi-Agent system were illustrated. These are significant methods and theories of Manufacturing Intelligence (MI). Data Mining (DM) and Knowledge Discovery (KD) are at the foundation of MI. Humans are immersed in data, but are thirsty for knowledge. With the wider application of database technology, a dilemma has arisen whereby people are ‘rich in data, poor in knowledge’. The explosion of knowledge and information has brought great benefit to mankind, but has also carried with it certain drawbacks, since it has resulted in knowledge and information ‘pollution. Facing a vast but polluted ocean of data, a technical means to discard the bad and retain the good was sought. Data Mining and Knowledge Discovery (DMKD) was therefore proposed against the background of rapidly expanding data and databases. It is also the result of the development and fusion of database technology, Artificial Intelligence (AI), statistical techniques and visualization technology (Fayyad U., 1998). DMKD has become a research focus and cutting-edge technology in the field of computer information processing (Jef Woksem, 2001). The development background, conception, working process, classification and general application of DM and KD are firstly introduced in this chapter. Secondly, basic functions and assignment such as prediction, description, data clustering, data classification, conception description and visualization processing are discussed. Then the methods and tools for DM are presented, such as the association rule, decision tree, genetic algorithm, rough set and support vector machine. Finally, the application of DMKD in intelligent manufacturing is summarized.

Ontologies Application to Knowledge Discovery Process in Databases

2009 ◽  
pp. 508-516
Author(s):  
Héctor Oscar Nigro ◽  
Sandra Elizabeth González Císaro
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Prior Knowledge ◽  
Contextual Information ◽  
Discovery Process ◽  
Whole Process ◽  
Definition Of

Nowadays one of the most important and challenging problems in Knowledge Discovery Process in Databases (KDD) or Data Mining is the definition of the prior knowledge; this can be originated either from the process or the domain. This contextual information may help select the appropriate information, features or techniques, decrease the space of hypothesis, represent the output in a more comprehensible way and improve the whole process.

scholarly journals Metode Decision Tree Algoritma C.45 Dalam Mengklasifikasi Data Penjualan Bisnis Gerai Makanan Cepat Saji

2018 ◽  
Vol 3 ◽  
pp. 1
Author(s):  
Eka Pandu Cynthia ◽  
Edi Ismanto
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Knowledge Discovery ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Fast Food ◽  
Knowledge Discovery In Databases ◽  
Decision Tree Algorithm ◽  
Food Outlets ◽  
Sales Company

Advances in technology and information currently produces smart innovations in business, which can be called business intelligence. One that we can use is Data Mining technology in digging useful information from sales company data warehouse. The purpose of this research is to apply data mining decision decision tree algorithm C4.5 on fast food outlets business and expected to provide information in the form of sales information about food menu that most liked by customers and less popular (bestselling and less in demand). The methodology used in classifying the sales of this research uses the steps of Algorithm C.45, The process uses five steps in KDD (Knowledge Discovery in Databases), which perpetuates activities such as pre-processing, transformation, data mining, interpretation and evaluation. In addition to performing calculations manually, this research case is also tested using Rapidminer application. From the results of the experiment to find data from the sales data of fast food outlets using algorithm C4.5 results of entropy and the highest gain is 1.501991 on the Food Menu attributes on manual calculations. When using the Rapidminer application the results of the results tree as shown in Figure 3.2. Price (s) - Sold Out - Food Menu (Bento Rice = Less Selling, Chest = Laris) Weight (weight) each attribute: Price (0.738), Menu Type (0.067), Sold Number (0.156), Sales Status (0.040).

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

2019 ◽  
Vol 1 (1) ◽  
pp. 121-131
Author(s):  
Ali Fauzi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Direct Investment ◽  
Capital Investment ◽  
Export Market ◽  
Added Value ◽  
Knowledge Generation ◽  
Management Scenarios ◽  
Data Mining Techniques ◽  
Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

Cyberphysical Design Automation Framework for Knowledge-based Engineering

2013 ◽  
Vol 1 (1) ◽  
pp. 158-178
Author(s):  
Urcun John Tanik
Keyword(s):  
System Design ◽  
Design Process ◽  
Design Automation ◽  
Design Theory ◽  
Knowledge Bases ◽  
Knowledge Based ◽  
Knowledge Based Engineering ◽  
Cyberphysical System ◽  
Automation Framework

Cyberphysical system design automation utilizing knowledge based engineering techniques with globally networked knowledge bases can tremendously improve the design process for emerging systems. Our goal is to develop a comprehensive architectural framework to improve the design process for cyberphysical systems (CPS) and implement a case study with Axiomatic Design Solutions Inc. to develop next generation toolsets utilizing knowledge-based engineering (KBE) systems adapted to multiple domains in the field of CPS design automation. The Cyberphysical System Design Automation Framework (CPSDAF) will be based on advances in CPS design theory based on current research and knowledge collected from global sources automatically via Semantic Web Services. A case study utilizing STEM students is discussed.

Sign in / Sign up

Export Citation Format

Share Document