Using Data Mining Techniques for Decision Support Systems (original) (raw)
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AN IMPROVED DESIGN FOR ASSOCIATION RULE DISCOVERY SYSTEM FOR DECISION SUPPORT SYSTEM.pdf
The challenge of associating rules to data set of items has been an important area in data mining research. Recent challenges is how to improve the algorithms that have been used in discovering associations that seem complex but are more useful in decision making and analysis. This paper presents a design for association rule discovery for decision support systems using an improved C4.5 Algorithm. This research work provides an efficient discretization method for the data which aids the discretization of continuous data and improves the rule discovery process from the different data sets and data attributes.
Data Mining of Association Rules and the Process of Knowledge Discovery in Databases
2002
This book presents papers describing selected projects on the topic of data mining in fields like e-commerce, medicine, and knowledge management. The objective is to report on current results and at the same time to give a review on the present activities in this field in Germany. An effort has been made to include the latest scientific results, as well as lead the reader to the various fields of activity and the problems related to them.
An Improved Design for Association Rule Discovery System for Decision Support System
The challenge of associating rules to data set of items has been an important area in data mining research. Recent challenges is how to improve the algorithms that have been used in discovering associations that seem complex but are more useful in decision making and analysis. This paper presents a design for association rule discovery for decision support systems using an improved C4.5 Algorithm. This research work provides an efficient discretization method for the data which aids the discretization of continuous data and improves the rule discovery process from the different data sets and data attributes.
Knowledge Discovery: Enhancing Data Mining and Decision Support Integration
2005
There are six stages of data mining processes; business understanding, data understanding, data preparation, modelling, evaluation and deployment. The third and one of the most important stages in data mining process is the data cleaning and preparation stage. Data cleaning and pre-processing involve the creation of the relevant data subset through data selection, as well as finding of useful properties/features, generating new features, defining appropriate feature values and/or value discretization. However, data mining’s performance and result accuracy highly dependent on the format and the availability of data presented and also the computational data mining tools. Experts are involved in most stages of a data mining project described by the CRISP-DM [Chapman, 2000]. The most informative attributes that influenced the accuracy of data mining are computed prior or during the process of data mining. On the other hand, a complementary approach to such problem solving that does not rely on collecting observational data is decision making. In this approach the human decision maker builds alternative models and defines the preference ordering criteria. This information is then used to make a rational decision. This process can be supported by computational decision support systems. To improve the quality of decision support, better submodels are needed, modeling the underlying decision making processes in a more realistic way. In order to include as much information as possible, the submodels of the expert system are usually provided with a lot of parameters describing different aspects of the decision making, hoping that the characteristics that are truly important are included in the model. In the context of classification, those descriptive parameters are termed features or attributes, and the selection of a good set of features/attributes is of key importance in the design of good classification models that will be used afterwards by the expert system. The roles of experts in data mining and decision supports are different, but complementary [Lavrač and Bohanec, 2003]. In an integrated approach to data mining and decision support, the potential of experts can even better be exploited in all stages of the integrated problem solving process. The gap between the format of data as stored in the data sources and that required by newly developed data mining algorithms must be bridged before any novel machine learning and data modelling algorithms tools can be used to their full potential. Transforming this data into a format appropriate for mining is a key (and often very time consuming) phase of the data mining process called data preparation. In this dissertation, I present a comprehensive survey of existing research in integrating data mining and decision supports and techniques in improving the performance of data mining algorithms and decision support. After the survey, a research proposal is put forward to study and investigate the method of integrating data mining and decision support for better accuracy results produced by both data mining algorithms and decision support model. Finally, some preliminary work in this area is presented.
SIMULATION OF IMPROVED ASSOCIATION RULE DISCOVERY SYSTEM FOR DECISION SUPPORT
Abstract Recent computing transactions entails large sum of data which are retrieved, stored and used for operations. The data often contain association relationships which can be mined to aid management decision. We simulate a data mining system for the association of rule discovery using an improved C4.5 Algorithm. The system extracts data and its close relationships that will be used for decision making. The system analysis and its design was done using the Object - Oriented System Analysis and Design Methodology (OOADM). Weather data file was used to test run the system and results shows that mining associated rules in a large database is important in decision making. Algorithms and Designs in rule discovery associations that seem complex but very useful in making decisions need to be well implemented to be useful to users. This paper presents a simulation of an improved C4.5 Algorithm for association rule discovery system used in decision support. Java programming language is used for the implementation with Netbeans IDE. When the system was tested, users reported better usability, efficiency and clarity of results from the application. Keywords: - Rule Discovery, Data Mining, C4.5, Decision Tree,Algorithm, Association Rule
Data Mining Techniques in Association Rule : A Review
2014
Data mining may be seen as the extraction of data and display from wanted information for specific process intended to searching information. There are different systems, tools and software’s which are used to extract a relative data from a specific group of data. The greater part of data mining methods can manage distinctive information sorts. The paper gives a review and relative study on a percentage of the most widely data mining techniques being used today in normal life and commercial business. KeywordsData mining, Knowledge management, Data mining techniques, Data mining applications.
TJPRC, 2013
Data Mining refers to extracting or “Mining” knowledge from large amounts of data. Today’s Industrial scenario is having manifold of data which is data rich and information poor .The information and knowledge gained can be used for applications ranging from business management, production control ,and market analysis, to engineering design and science exploration. Data Mining can be viewed as a result of natural evolution of information technology. Association rule mining finds interesting association among a large set of data items. With massive amounts of data continuously being collected and stored. Many industries are becoming interested in mining association rules from their databases. The discovery of interesting association relationships among huge amounts of business transaction records can help in many business decision making process , such as catalogue design, cross marketing, and loss leader analysis.
A Comparative Study of SQL Based Approaches for Mining Association Rules over Relational DBMS
Abstract Data mining is known as a knowledge discovery tool in databases. It is the automated extraction of implicit, understandable, previously unknown and useful information from large databases. Association rules are used to identify relationships among a set of items in database based on co-occurrence of data items. This paper presents the concepts and research situations of data mining, association rule and alternative approaches for integrating mining process in DBMS. The paper presents a comparison analysis between two architectural alternatives of data mining. These approaches used stored procedures and user defined functions (SQL-OR) and approaches based on pure SQL (SQL-92 standard). The paper presents analytical study for association rule mining of these approaches. The results collected from this comparison can certainly be converted into meta-data that can be used by a mining optimizer to choose a good available approach to mine the input data. Recommendations and remarks have been highlighted.
Data Mining and Knowledge Discovery
Opportunities, Limitations and Risks, 2004
In today's business world, the use of computers for everyday business processes and data recording has become virtually ubiquitous. With the advent of this electronic age comes one priceless by-product-data. As more and more executives are discovering each day, companies can harness data to gain valuable insights into their customer base. Data mining is the process used to take these immense streams of data and reduce them to useful knowledge. Data mining has limitless applications, including sales and marketing, customer support, knowledge-base development, not to mention fraud detection for virtually any field, etc. "Data mining," a bit of a misnomer, refers to mining the data to find the gems hidden inside the data, and as such it is the most often-used reference to this process. It is important to note, however, that data mining is only one part of the Knowledge Discovery in Databases process, albeit it is the workhorse. In this chapter, we provide a concise description of the Knowledge Discovery process,
A perspective review of the role of Data Mining in Knowledge Discovery
Data mining in warehouse and Knowledge discovery in databases are no doubt fast growing fields that is motivated by strong research interests, vital practical, economic and social needs. There is need for clearer understanding of the advantages of using different data mining tools and techniques — and knowing what data mining does — can help customer relations managers or the beginner auditors provide recommendations that improve business trends, processes and reduce the cost of acquiring new customers, generate new leads and improve the sales rate of new products and services, or discover fraud. However, whether you are a beginner internal auditor or a seasoned veteran manager looking for a refresher, gaining a clear understanding of what data mining does and the different data mining tools and techniques available for use can improve audit activities and business operations across the board. In this paper, we emphasizes on basic data mining concepts and techniques for uncovering interesting data patterns hidden in large data sets. The paper starts with an overview of data mining and knowledge discovery, common approaches and process of knowledge discovery, its importance in relation to decision support system, and finally a step by step algorithms and models of data mining to help a company easily achieve their goals in this field by fulfilling the urgent needs stated above. The implementation methods discussed are particularly oriented toward the development of scalable and efficient data mining tools.