Data Mining Abstract Data mining is a combination of database and artificial intelligence technologies. Although the AI field has taken a major dive in the last decade; this new emerging field has shown that AI can add major contributions to existing fields in computer science. In fact, many experts believe that data mining is the third hottest field in the industry behind the Internet, and data warehousing. Data mining is really just the next step in the process of analyzing data. Instead of getting queries on standard or user-specified relationships, data mining goes a step farther by finding meaningful relationships in data. Relationships that were thought to have not existed, or ones that give a more insightful view of the data. For example, a computer-generated graph may not give the user any insight, however data mining can find trends in the same data that shows the user more precisely what is going on. Using trends that the end-user would have never thought to query the computer about. Without adding any more data, data mining gives a huge increase in the value added by the database. It allows both technical and non-technical users get better answers, allowing them to make a much more informed decision, saving their companies millions of dollars. Introduction "Data mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques" (SPSS). However, really data mining turns databases into knowledge bases which is one of the fundamental components of expert systems. Instead of the computer just blindly pulling data from a database, the compu... ... middle of paper ... ... database, the individual lengths of the hay represent your data fields, and the needles represent data fields with a relationship worth more to you than all the hay put together" (Newquist). Works Cited Mega Computer. "Reasons for the growing popularity of data mining." Online. Internet. 3 Oct. 1997 Available: http://www.megaputer.ru/dmreason.html. Lindsay, Clark. "Data Mining." Online. Internet. 3 Oct. 1997 Available: http://msia02.msi.se/~lindsay/datamine.html Newquist, H.P. "Data mining: The AI metamorphosis." Online. Internet. 3 Oct. 1997 Available: http://www.dbpd.com/newquist.html. Pilot Software. "An Introduction to Data Mining." Online. Internet. 3 Oct. 1997 Available: http://www.pilotsw/dmpaper/dmindex.htm. SSPS. "SSPS' Approach: Open Comprehensive Data Mining." Online. Internet. 3 Oct. 1997 Available: http://www.spss.com/datamine/ocdm.html
To make the best of the seemingly untappable resource, a new field of data extraction, visualization, management and manipulation has come about – Data Analytics or Data Science. People who indulge in this data mining
Abstract: In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is the intelligence exhibited by machines or software. It is the subfield of computer science. Artificial Intelligence is becoming a popular field in computer science as it has enhanced the human life in many areas. Artificial intelligence in the last two decades has greatly improved performance of the manufacturing and service systems. Study in the area of artificial intelligence has given rise to the rapidly growing technology known as expert system. Application areas of Artificial Intelligence is having a huge impact on various fields of life as expert system is widely used these days to solve the complex problems
Data can give you quite a bit of information about your customers. By examining it, you will be able to begin to see patterns and learn the habits of your customers. This could mean that you are able to provide the correct number of products at the perfect time instead of having a shortfall or being left with additional stock long after interest has fallen in the product.
In the day to day job of a Network Manager at Bellsouth there are many decisions which have to be made. One such decision opportunity arose about one week ago. The question was what to do with a major cable which is in the way of a guard rail that the Department of Transportation is installing. In this paper, the decision on what to do with this cable will be solved using a decision tree. The discussion will include the major factors involved in making the decision and also show how the final decision was made.
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
There are various kinds of definitions about what data mining is. The authors in [1] define data mining as “the process of extracting previously unknown information from (usually large quantities of) data, which can, in the right context, lead to knowledge”. Data mining is widely used in areas such as business analysis, bioinformatics analysis, medical analysis, etc. Data mining techniques bring us a lot of benefits. Business companies can use data mining tools to search potential customers and increase their profits; medical diagnosis can use data mining to predict potential disease. Although the term “data mining” itself is neutral and has no ethical implications, it is often related to the analysis of information associated with individuals. “The ethical dilemmas arise when data mining is executed over the data of an individual” [2]. For example, using a user’s data to do data mining and classifying the user into some group may result in a variety of ethical issues. In this paper, we deal with two kinds of ethical issues caused by data mining techniques: informational privacy issues in web-data mining and database security issues in data mining. We also look at these ethical issues in a societal level and a global level.
A data stream is a real time, continuous, structured sequence of data items. Mining data stream is the process of extracting knowledge from continuous, rapid data records. Data arrives faster, so it is a very difficult task to mine that data. Stream mining algorithms typically need to be designed so that the algorithm works with one pass of the data. Data streams are a computational challenge to data mining problems because of the additional algorithmic constraints created by the large volume of data. In addition, the problem of temporal locality leads to a number of unique mining challenges in the data stream case. The data mining techniques namely clustering, classification and frequent pattern mining are applied to extract the knowledge from the data streams. This research work mainly concentrates on how to find the valuable items found in a transactional data of a data stream. In the literature, most of the researchers have discussed about how the frequent items are mined from the data streams. This research work helps to find the valuable items in a transactional data. This is a new research idea in the area of data stream frequent pattern mining. Frequent Item mining is defined as finding the items which are occurring frequently and above the given threshold. Valuable item is nothing but finding the costliest item or most valuable items in a data base. Predicting this information helps businesses to know about the sales details about the valuable items which guide to make important decisions, such as catalogue drawing, cross marketing, consumer shopping and performance scrutiny. In this research work, two new algorithms namely VIM (Valuable Item Mining) and TVIM (Tree based Valuable Item Mining) are proposed for finding the...
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
All companies obtain information on their customers or on their product. All these information may help a business to develop new strategies. They can also forge ahead by treating these big data. All companies have in their possession those information, so use it can be very useful. A company can identify for example their weaknesses and can improve their strategy to become the best on the market. It will help to create more opportunities for the company and maybe create a real competitive advantage.
Big data is a concept that has been misunderstood therefore I will be writing this paper with the intentions of thoroughly discussing this technological concept and all its dimensions with regard to what constitutes big data and how the term came about. The rapid innovations in Information Technology have brought about the realisation of big data. The concept of big data is complex and has different connotations but I intend to clarify its functions. Big data refers to the concept of a collection of large and complex amounts of data that are found extremely difficult to notate or even process by most on-hand devices and database technologies.
Data Mining (DM), or Knowledge Discovery is extraction of implicit, hidden trends, previously unknown, and useful information from data. DM research adopted many techniques from research areas like artificial intelligence, statistics and machine learning.
...at to expect from our society and consumers is very key in the business world. With business intelligence and Data Mining strategies and skills, companies can have that extra competitive edge which will in turn increase profits and market share. The skills gained by those employees who specialize in the BI and DM fields will continue to be top-notch assets to companies and based on the salary trends, they will continue to have increasing compensation. Businesses that implement BI and DM effectively will dominate their markets and stay ahead of the curve.
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?
The data mining process will use the mapping function which involved the decision tree and also the neural network to develop. It needs the web server and the database server to be constructed in an operating database to record the browsing route of the users. The data mining will use to identify the user’s information and classify them into different classes using decision tree.