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... ... middle of paper ... ... the underlying concept of data changes over time. Concept-evolution occurs when new classes evolve in streams. Feature-evolution occurs when feature set varies with time in data streams. Data streams also suffer from scarcity of labelled data since it is not possible to manually label all the data points in the stream. Each of these properties adds a challenge to data stream mining. This valuable item mining helps to find the most valuable items of a transactional database. This can be achieved by providing the cost of an individual item and assigning an individual threshold for each and every item in a transaction. This gives the information about the particular item will be sold at particular time. This information also provides whether the business is a profitable one or not. Through this valuable item mining the owner can improve his/her business strategy.
Evolution is described, as being the change that occurs on a genetic level when a new generation spouts from an ancestral population. Change is destined to happen. That is why in the science of biology the word evolution means descent with modification. Through various factors such as the temperature of the environment, humidity, and altitude a species will adapt to survive and will eventually pass on genetic traits that help the species next generation survive.
The first part of the evolution theory is evolution itself. Evolution itself is the idea that a species undergoes a genetic change over time to evolve into something that is very different. These differences are seen in our DNA and are considered mutations at first but slowly become the norm.
CarMax faces challenges from several fronts that could threaten to disrupt their growth plans and their position as a disruptor in the used car market. The biggest challenge they face is being able to continuously secure a study supply of high quality used cars, due to the extremely competitive nature of the used car market. CarMax offers cutting edge technology to help the company identify buying trends, pricing trends, and consumer preferences down to the zip code that gave them a large competitive advantage, as “data mining” has matured and competitors have developed their own software tools, eroding the competitive advantage to CarMax.
One of the biggest problems that affect everyone is data aggregation. The more the technology develop, the powerful and dangerous it gets. Today there are many companies that aggregate a lot of information about us. Those companies gathering our data from different sources, which create a detailed record about us. Since all services have been computerized whether it is handled directly or indirectly through computers, there is no way to hide your information. We used computers, because they are faster, better, and accurate more that any human being. It solved many problems; however, it created new ones. Data does not means anything if it stands alone, because it is only recoded facts and figure, yet when it organized and sorted, it become information. These transformed information. Data aggregation raises many questions such as, who is benefiting from data aggregation? What is the impact on us (the users)? In this paper I will discuses data aggregation and the ethics and legal issues that affect us.
However, mutation is random in the evolution, and provides raw material for natural selection, genetic drift, and gene flow to work on.... ... middle of paper ... ... Evolution is an ongoing process and the evolution is made up of many different processes. It allows species to become what they are, how they act, and what they will become.
Clustering algorithms can be categorized based on their cluster model. The most appropriate clustering algorithm for a particular problem often needs to be chosen experimentally. It should be designed for one kind of models has no chance on a data set that contains a radically different kind of models. For example, k-means cannot find non-convex clusters. Difference between classification and clustering are two common data mining techniques for finding hidden patterns in data. While the classification and clustering is often me...
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.
This chapter gives the overview of the Association Rule Mining. It gives the importance of the Market Basket Analysis and its usefulness in increasing the sales of the supermarket. This chapter also provides an overview of the data mining process used in market basket analysis and the proposed approaches. The works of a few scientists are cited and utilized as proof to confidence the ideas clarified in the theory. Every such proof utilized is recorded as a part of the reference area of this thesis.
In the beginning, businesses used information technology for automating the processes primarily to reduce labor costs. Subsequently, information technology is used for delivering information with speed and accuracy.
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
and is especially popular among eBay customers. Fig.1 briefly illustrates Company’s business. The system enables its
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.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
Mining is the process of providing synchronised & secure network system for transaction using computer & digital technology controlled by different ‘Data Centres’.
This project implements the ID3 algorithm for reading data stored in multiple data sources. It comes under the broader topic of data mining. Data mining is the reading and processing of useful data from different sources. Essentially, the process of hunting for required or useful data contained in a large database is characterized as data mining. In the case of logical outcomes, a decision tree is predominantly used for analysis. The advantages of using a decision tree are that it is easier to model, analyse, and manipulate accordingly. The ID3 algorithm is used to generate a decision tree from a certain set of data.