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.
ETL is a three-step process which stands for Extract-Transform-Load. This process comprises of: extracting the desired data from a source, transforming the extracted data into a specific format, and loading the transformed data into a destination such as a data warehouse (Haag & Cummings, 2013). After the ETL process is performed, data-mining tools can be used to turn this data into useful information. For the first three questions, the database would need to capture each checkout price, how many items are purchased, the individual price of each item, and if the item is discounted or full MSRP. This specific data will likely originate from a customer oriented database that will then flow into the data warehouse for full ETL. For YTD profits, the database would need to capture all purchases, sales, profits, and expenses from the current year. Sport T’s company data will originate from an in-company database which focuses on business expenses and profits. In solving customer satisfaction, the KPIs to consider would be survey questions and answers from responding customers as well as customer opinion on what can be improved. For customer surveys, we will ask
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.
Analyzing data to detect unknown trends and patterns will aide business decisions for the future.
Since both consumers and businesses advantage from the use of data mining, each party has to honour the right of the other one in order to keep an ethical function of the data mining relationship between the two of them. Long ago, data mining was only about essential and voluntary information collected from customers who were aware that their information is being gathered. Nowadays, the ethical issues raised are whether the data collected will be used against customers’ rights, and whether it will become a part that is accessible in the future by others. The strategies proposed by Payne and Trumbach, with regard to Data mining(1) and consumers’ information, propose that in the right moral structure, data mining can be ethically effective and protective to consumers’ right. Six principals are needed for a productive ethical data mining strategy: anonymity, disclosure, choice, time limits, trust and accuracy of data (Payne & Trumbach, 2009).
Data stream mining is a stimulating field of study that has raised challenges and research issues to be addressed by the database and data mining communities. The following is a discussion of both addressed and open research issues [19].
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...
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
n data mining, trends and patterns are identified on a huge set of data to discover knowledge. In such analysis, varieties of algorithms exist for extracting knowledge such as clustering, classification and association rule mining. Thus, association rules mining one domain for delivering knowledge on complex data. Moreover, the basis of the discovered association rules is usually determined by the minimum support s % and minimum confidence c% to represent the transactional items in database D. Thus, it has the implication of the form AB, where A is the antecedent and B is the consequent. The problem with such display of rules is the disclosure of sensitive information to the external part when data is shared. Hence Privacy Preserving in Data Mining (PPDM) related to Association Rules emerges.
ADP, Automatic Data Processing was founded in 1949 by a business man from New Jersey, named Henry Taub. During this time the company we known by Automatic Payroll Inc. The first account the company landed was New Era Dye and Finishing, both business resided in New Jersey. In 1958, the name was changed and finalized to Automatic Data Processing, and incorporated new technology such as punch card machines to time stamp hours of employees, check printing machines, and mainframe computers. ADP was a private company until 1961 it went public. ADP has been around now for 65 years now.
Data mining is the practice of gathering data from various sources and manipulating it to provide richer information than any of contributing sources is able to do alone or to produce previously unknown information. Businesses and governments share information that they have collected with the purpose of cross-referencing it to find out more information about the people tracked in their databases.
Clustering algorithms are used to discover structures and groups in the data, e.g. it classifies the data belongs to which group
However, a DSS tool is Online Analytical Processing (OLAP) –Decision support system is an interactive computerized system which gathers and presents for business purposes from various sources (webopedia.com, 2014). OLAP is a tool that enables the user to analyze different data dimensions. It provides time series as well as trend analysis views. OLAP tools are used by analysts where they employ relatively simple techniques which include induction, deduction as well as pattern recognition to so as to derive new information as well as insights. OLAP is also used in data mining using OLAP server which sits between a database management systems and a client. For example, Infosys – an information technology consultancy, recommended one of the clients to use OLAP solutions as a supply chain analytic solution which contributed 30% of its gross revenue.
Today, the topic of data mining has much interest in government, business, and research circles. With the growth of computer use within these areas has also come a greater desire to let the computers do the work that used to be done by humans. The problem, nowadays, is that the data that needs to be analyzed has become too large and cumbersome for one person or even teams of people to envision tackling without help from computers. These computers are no longer mere crunchers of numbers but now they find the patterns that the humans used to find. From this growth has arisen a vast body of knowledge concerned with this process of data analysis. As with much other information, the Internet is employed to make available the ever-growing body of information on this topic. Many general sources of information [a,b,c] are now online. These are updated and expanded upon almost a constant basis. The use of the Internet to disseminate and collect information is itself a consideration in this field. The amount of information is expanding at such a rate that old methods of information disposal, such as paper journals and b...
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.
Moreover, E-commerce has widely recognized nowadays among people. Therefore such data should be secure in databases and privacy of data should be maintained.