Abstract The paper explains that datamining is a process whereby enterprises or organizations in any industry approach their respective data and databases in a more constructive and targeted manner to produce actionable business strategies. As some researchers have observed, datamining and data warehousing are becoming more prevalent because of the large quantities of data stored in various systems and the number of business decisions made based on the data.
From the Paper "Thus, data mining and data mining techniques have risen to prominence with the elevation in importance of databases and, more recently, the development of data warehouses that have changed the complexion of industry in all sectors. Data mining and data warehousing solutions have been especially important in customer relations management (CRM) and in the healthcare industries for example."
Abstract This paper examines datamining in e-commerce and discusses the various types of modeling used to make the data meaningful to e-tailers. The advantages and pitfalls of datamining and an explanation of how it has transformed e-commerce are detailed. The paper includes an abstract and table of contents.
From the Paper "Data mining as applied to e-commerce is a breakthrough technology that can gather information in an automated fashion and build models used to predict customer purchasing decisions with remarkable accuracy ..."
This paper discusses the use of datamining, a technique using sophisticated computer software to scour a company's database looking for specific management information.
Abstract This paper defines datamining to help senior management to manage and direct the company more effectively. The author points out the types of data. The paper reviews methods of datamining including the software.
From the Paper "Domestic and global business competition is fierce. Companies must look for new tools to give them a competitive advantage. Technology has added an additional layer of competitive tools commonly called data mining technology or data mining software. For many firms seeking to improve their competitive edge, the ability to perform data mining is rapidly becoming a necessity rather than a luxury. Data mining involves the extraction of hard to find information from large databases that companies normally maintain. Data mining tools can help businesses predict future ..."
Tags:datamining, techniques, tools, models, practical applications, competitive advantage, public and private sector applications, widespread acceptance
Abstract Datamining has become a very important concept today and is used by companies all over the world to increase their profits and target the right market. The paper talks about the different aspects of datamining, tools used, and future trends in datamining. Datamining benefits are discussed in detail, and an entire discussion related to the trends in datamining is presented.
1-Background
2-Introduction
3-DataMining Growth and Tools
4-The DataMining Process
5-DataMining Market Place Trends
6-The Data in DataMining and Meta Data 7-Types of DataMining Problems
8-Privacy and Ethical Sensitivity in DataMining Results
9-Future Prospects of DataMining 10-Works Cited
From the Paper "Data, particularly in the vast diversity and immense quantity that it is available to modern business, was till recently almost very hard to find and understand. Yet, the comprehension of data is the most crucial step to extracting the knowledge that it contains. The scenario has drastically changed today where data is much more easily available and has become more "meaningful" with the utilization of Data Mining. Today, technology offers business managers powerful new tools for gleaning knowledge from data-the essentials of data mining. Data mining has become increasingly important to mainstream companies to become more competitive both in their workings and their customer based relationships. Data mining, as such is of great interest because it is imperative for organizations to grasp the competitive value of information contained within their data repositories. There are a number of pertinent benefits of data mining. First of all, data mining provides the tools and techniques that are essential for optimization of customer relationships. Secondly, data mining provides an automatic method of discovering patterns in data. Thirdly, but not the least, data mining tools can identify the relationships that are actually present in historical data."
Abstract This paper relates that the use of datamining, its adjunct technologies for text mining and the ability to interpret, analyze and create linguistic models from unstructured content is revolutionizing the concept of datamining away from being purely used for structured content in data warehouses to now encompass unstructured content found throughout organizations globally.
The paper then provides insights into various areas of datamining, and the currently high levels of growth analytics use and applications software are experiencing as a result.
Outline:
Executive Summary
Using DataMining in Business Research
Exploring the principles of DataMining in Business Research
Predictive Methods in DataMining
From the Paper "A second predictive approach is called deviation detection. The purpose of this method is to discover the most significant changes in data from previously measured or median values. An example of the type of use for this predictive approach would be the development of strategies for selling tickets to frequent flyers who booked months in advance versus those that consistently book within a few weeks of their departure. A third approach to using data mining to predict future outcomes is using the classification approach, or technique. This predictive approach of classification uses a collection of records (training set) -- each record contains several attributes, one of them is the class (Ng & Han, 10). The task is to find a model for the class attribute as a function of other attributes, so, after that, previously unknown records can be assigned a very accurate class."
Abstract The paper discusses how datamining was employed to customize offerings for goods and services to the paper's author during recent weeks. The paper explains how the avalanche of marketing materials received as a result of datamining can be traced back to many everyday activities. The paper therefore shows that by employing datamining techniques of tracking customer transactions and analyzing the statistical models for anticipated behavior, companies are able to deliver timely, pertinent, and coordinated messages and value propositions to customers and prospects.
Outline:
Introduction
Transactions
Conclusion
From the Paper "One method for helping a company decide how best to select and interact with customers is to collect and analyze vast amounts of information about people's preferences. The detection and use of statistically relevant patterns to build models that predict customer behavior is the process of "data mining". For a data mining process to be successful, database marketers must first identify market segments containing customers or prospects with high-profit potential. They then build and execute campaigns that favorably impact the behavior of these individuals (Thearling, 2001). This paper will discuss how data mining was employed to customize offerings for goods and services to the paper's author during recent weeks."
Abstract Datamining is the extraction of hidden predictive information from large databases. This paper examines the effect that datamining has on the current corporate climate. It defines datamining and examines the scope of its existence and effects on overall industry and the rest of the world. The paper also explains the basics of the technology behind datamining and how these tools will interact with localized software. Examples of how datamining technology can be profitably used, as well as how it will use the data warehouse architecture to evolve existing software to develop new ways to collect and interpret information, is also looked at.
From the Paper "Model building itself is not a new technology; it is in fact something that has been around for a very long time. Since the beginning of computer technology, modeling has been a method to finding solutions. Computers work just as humans do by collecting information from a variety of differing situations and attempting to put it together in such a way that makes sense. With computers, there are more resources as well as faster integration of the information so the model building process is easy, fast and efficient. It also is much more complex than anything that a human can build which means the answer is in more depth and more accurate."
Abstract This paper presents an overview of datamining tools commercially available today. These tools are invaluable in helping commercial ventures, scientists, economists, medical practices, and even weather forecasters detect patterns and data sets in vast quantities of information that they have collected. After an overview of what datamining is and how it is utilized, the writer focuses on specific datamining tools on the market. A description of characteristics and the leading products of the type of tool are examined. Finally, a detailed look at a specific product, which uses neural network-based datamining tools, is examined in depth.
From the Paper "Data mining is the process of seeking and extracting knowledge buried in large volumes of raw data. The importance of collecting data that reflects business or scientific activities is well recognized today. (Brodley, Lane, Stough 1999) Most large and mid range companies now utilize various commercially available data warehousing software for collecting and managing the large quantities of information that they collect. Before data mining technology, the bottleneck in turning raw data into useful information was how to accurately and quickly extract knowledge from the collected raw data. Analysis by humans without special tools cannot make sense of enormous volumes of data that require processing in order to make informed business or scientific decisions. Data mining automates the process of finding relationships and patterns in raw data and delivers results that can be either utilized in an automated decision support system or assessed by human analysts. (Brodley, Lane, Stough)"
Abstract This paper examines the InfoWorld's article by Paul Krill, "Microsoft Pushes DataMining in Business Intelligence Protocol" and relates the business merits of DataMining. The paper also explores other resources on the topic that discuss its impact on business and individuals, with particular attention the Fortune 500 corporations. The paper provides a history of datamining in order to foresee its future.
From the Paper "There is a great deal of flexibility which businesses who use data mining will have upon their marketing campaigns. By knowing the customer almost as well as the customer knows him or her self, retail businesses can adjust their sales tactics to match the needs and desires of the customers. Individuals will have more choice, and more pleasing choice too. Companies will have more options for enticing customers, and less money can be spent on sending out independent researchers ? since the data will already be at hand."
Tags: paul, krill, microsoft, xml, soap, information, location, business
Abstract This paper examines the practice of datamining. It outlines what datamining is and why it is engaged in. It also considers how datamining raises privacy concerns.
Abstract This study examines the use of advanced techniques of data clustering in algorithms that employ abstract categories for the pattern matching and pattern recognition procedures used in datamining searches of web documents. The paper discusses the significance and purpose of the study. The author gives exact methodology, organization and statistical results of the study. It also evaluates implementation of data clustering for web based searches and its feasibility.
Table of Contents
Background of the Problem
Significance of the Study
Definitions
Overview of the Methodology
Organization of the Study
Purpose of the Study
Background and a Review of Literature
Alternative Solutions
Feasibility Tests
Evaluation and Implementation
References
From the Paper "Documents are commonly represented as vector space models. In this model, each document is represented by a point in the space that roughly corresponds with the union of the primary words in the document. The process includes filtering out common words (such as pronouns and conjunctions), ignoring words that are unique to the document, and words are stemmed in order to reduce them to canonical form. Once this is done , the document can be expressed in the form of vectors, and then those vectors can be used to plot a pint in virtual space that represents that specific document. The words of the document are also weighted; without this provision, the more commonly a word appears in a document, the more important it is considered (which is often not really the case). Conversely, words that appear infrequently in a document are considered discriminatory- they serve as a distinguishing feature for the document in question.[10]"
Abstract This paper explores data warehousing in terms of datamining with intelligent agents such as bots and ants and clarifies the ethical dilemma posed by the use of such data.
From the Paper " Data warehousing is no longer simply a storage system for data. Today's data warehousing involves innovative technological software, automated agents known as intelligent agents robots-or bots and ants. These agents ..."
Abstract The paper discusses how the many advances in data and text mining are already revolutionizing the librarian profession. The paper explores how the ability of datamining tools to extract, transfer and load (ETL) massive amounts of data at a single time, is changing how all tasks in an organization get completed.
Outline:
Executive Summary
Content Integration Is Key
DataMining i) Principles of DataMining ii) DataMining Timeline
DataMining Implications for Librarianship
Text Mining i) Text Mining Timeline
ii) DataMining versus Text Mining iii) Mining Blogs: An Example of How Text Mining Works
Text Mining Implications for Librarianship
Conclusion
From the Paper "At the intersection of text mining, linguistic analysis, statistical analysis, and latent semantic indexing techniques (Wikipedia Latent Semantic Indexing 2006). is the future of text mining that has the power to discover and report trending in highly unstructured content. At the center of text-mining's' rapid growth is the increasing sophistication of Natural Language Processing (CRM Buyer 2005). IBM and their significant research efforts in natural language processing are well documented on their website, as are the efforts and investments Microsoft is making."
Tags: latent, semantic, indexing, clustering, hosted, applications, Island, Data, Attensity
Abstract This paper discusses three articles on data collection and analysis tools and their applications. This includes datamining, data warehousing and software packages used in the collection. This paper also analyzes the needs of the business upon which the correct data collection and analysis tools are selected.
From the Paper "Business today has more and more need for external consultants to use data collection and analysis tools in order to make assessment of business operations and processes. Many of the methods used today are computer-based, including software that does much of the job but still requires an able human operator to make decisions and input the correct information. Various analysts have made assessments of these methods to see how they are used and how effective they may be. Such tools are also used for analyzing performance in education, for assessing public programs, and for other tasks requiring a decision as to the value of a program or process. Bielski (2001) discusses the use of CRM, or Customer Resource Management system, which is used to track customer purchases while providing access to customer information using the computer. "
Abstract This paper will discuss the involvement process in datamining, and how it works with the relational database management system used in today's world. By discovering how this works in the scope of computer data warehousing process, we can see how this function is essential to understanding the data in computers. With these tools for understanding data processes in this arena, the idea of learning patterns in programming on the most up-to-date levels.
