Using Data Mining Techniques for Decision Support Systems (original) (raw)
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.
USE OF DATA MINING TECHNIQUES IN ADVANCE DECISION MAKING PROCESSES IN A LOCAL FIRM
2015
In today's competitive world, organizations need to make the right decisions to prolong their existence. Using non-scientific methods and making emotional decisions gave way to the use of scientific methods in the decision making process in this competitive area. Within this scope, many decision support models are still being developed in order to assist the decision makers and owners of organizations. It is easy to collect massive amount of data for organizations, but generally the problem is using this data to achieve economic advances. There is a critical need for specialization and automation to transform the data into the knowledge in big data sets. Data mining techniques are capable of providing description, estimation, prediction, classification, clustering, and association. Recently, many data mining techniques have been developed in order to find hidden patterns and relations in big data sets. It is important to obtain new correlations, patterns, and trends, which are understandable and useful to the decision makers. There have been many researches and applications focusing on different data mining techniques and methodologies. In this study, we aim to obtain understandable and applicable results from a large volume of record set that belong to a firm, which is active in the meat processing industry, by using data mining techniques. In the application part, firstly, data cleaning and data integration, which are the first steps of data mining process, are performed on the data in the database. With the aid of data cleaning and data integration, the data set was obtained, which is suitable for data mining. Then, various association rule algorithms were applied to this data set. This analysis revealed that finding unexplored patterns in the set of data would be beneficial for the decision makers of the firm. Finally, many association rules are obtained, which are useful for decision makers of the local firm.
Association Rules in Data Mining
Data mining is motivated by the decision support problem faced by most large retail organizations. Association rule mining is finding frequent patterns, associations, correlations or casual structures among sets of items or objects in transactional databases, relational databases and other information repositories. It has various applications including market-basket data, analysis, cross marketing, catalogue design, and loss-leader analysis. For example, 98% of customers that purchase tires and auto accessories also get automotive services done. Finding all such rules is valuable for crossmarketing and attached mailing applications. In this paper presentation we will analyses the various data association rules and develop an insight into the implementation of these rules for better sales of a company. Moreover in data mining association rules are useful for analyzing and predicting customer behavior. We will also throw a light on Apriori Algorithm, which is probably the best known algorithm for learning association rules.Apriori is designed to operate on databases containing transactions. For example: Collection of items bought by customers or detail of a website frequentation. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets, as long as those item sets appear sufficiently often in the database.
Decision Support System using Data Mining Method for a Cross Selling Strategy in Retail Stores
A sales transaction dataof a retail company which is collect edevery day is enormous. Very large data will bemore meaning fultoin crease the company's profitsif itcanbe extracted properly. Based on the research resultsof Andhika, et al[1], ZhangandRuan[6], Herera et al [7], Witten [11], explained that one of the methods that can gather information from the transaction data is the method of association. With this method it can be determined the patterns of transactions performed simultaneously and repeatedly. Thus, it can be obtained amodel that can be used as a reference for cross selling sales strategy. The purpose of this research is to apply data mining association methods of data mining by using apriori algorithm to create a new sales strategy for cross selling. Based on calculations, Association Rule is implemented by applying Confidence value=0.8 while the value of Support=0.1 of the defined minimum value, the total result are 77 rules. 1. INTRODUCTION Decision Support System (DSS) began to attract attention among programmers and systems analysts. These systems assist decision-making by managing data and using certain models to solve the problem. Decisionsupport systems to be special because it is able tosolv ethe problem of un structured or semi-structured. In many areas, a decision support system has many perceived benefits and dependency to use the system in creasing with the increasing complexity of the data management proces seach information system. Application of the DSS to a business interest plays a very important role, such as to provide advice in the preparation of cross-selling strategy atretail stores. One method that can be used to solve the seproblemsis by using association method of apriori algorithms. The study on the application of the method of Apriori Association has been done by previous studies with a variety of objects. Research conducted by Andhika et al [1] entitled Excavation Association Patterns in Data Warehouse Agent Manufacturing Using Microsoft SQL Server (Case Study: PTXYZ) aimstocreatean integrated system, using the data warehouse and association method of rule mining, that found a pattern of sales transaction data from previous periods regarding the relationship between-a variable that is knowntendency of the product to be purchased by the customer in conjunction with the specific product. The method used is the design ofthe data, starting from the formulation of the problems encountered, and then do a search the required data. Once collected, the data is filtered and transformed that into a form consistent database. Further more, applying association rule mining using Microsoft Visual Studio. The end result is an
Data Mining as Support to Knowledge Management in Marketing
Business Systems Research Journal, 2015
Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in ...