1.2 What is Data Mining?
Structure of Data Mining
Generally, data mining can be associated with classes and concepts. data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is the best analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among the dozen fields in large relational databases.
1.2.1 How Data Mining Works?
While large-scale information
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For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.
Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.
1.2.2 Data mining consists of five major elements:
• Extract, transform, and load transaction data onto the data warehouse system.
• Store and manage the data in a multidimensional database system.
• Provide data access to business analysts and information technology professionals.
• Analyze the data by application software.
• Present the data in a useful format, such as a graph or
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Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID). CART and CHAID are decision tree techniques used for classification of a dataset. They provide a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given outcome. CART segments a dataset by creating 2-way splits while CHAID segments using chi square tests to create multi-way splits. CART typically requires less data preparation than CHAID.
Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k=1). Sometimes called the k-nearest neighbor technique.
Rule induction: The extraction of useful if-then rules from data based on statistical
Privacy Preserving Data Mining (PPDM) was proposed by D. Agrawal and C. C. Agrawal [1] and by Y. Lindell and B. Pinkas [5] simultaneously. To address this problem, researchers have since proposed various solutions that fall into two broad categories based on the level of privacy protection they provide. The first category of the Secure Multiparty Computation (SMC) approach provides the strongest level of privacy; it enables mutually distrustful entities to mine their collective data without revealing anything except for what can be inferred from an entity’s own input and the output of the mining operation alone by Y. Lindell and B. Pinkas in [5], J. Vaidya and C.W.Clifton in [6]. In principle, any data mining algorithm can be implemented by using generic algorithms of SMC by O.Goldreich in [7].However, these algorithms are extraordinarily expensive in practice, and impractical for real use. To avoid the high computational cost, various solutions those are more efficient than generic SMC algorithms have been proposed for specific mining tasks. Solutions to build decision trees over the horizontally partitioned data were proposed by Y. Lindell and B. Pinkas in [5]. For vertically partitioned data, algorithms have been proposed to address the association rule mining by J. Vaidya and C.W.Clifton in [6], k-means clustering by J. Vaidya and C. Clifton in[8], and frequent pattern mining problems by A.W.C. Fu, R.C.W. Wong, and K. Wang in [9]. The work of by B. Bhattacharjee, N. Abe, K. Goldman, B. Zadrozny, V.R. Chillakuru, M.del Carpio, and C. Apte in [10] uses a secure coprocessor for privacy preserving collaborative data mining and analysis. The second category of the partial information hiding approach trades pr...
This section briefly describes the technical terms regarding online marketing and SEO, which will be encountered in later chapters.
Define the three primary types of decision-making systems, and explain how a customer of Actionly might use them to find business intelligence.
The company can combine the customer location information with the profile of the customer and their preferences and also consider providing data analytics services to other industries like retail, advertising, health care, financial services and public services etc. A lot of telcom providers like Verizon, Telefonica, AT&T and Sprint are making use of these opportunities and monetizing data and thereby increasing their revenue.
By developing predictive analytics, each store can previse demand at specific time periods and decide how many cashiers are necessary to optimize the checkout speed at the counters. In Walmart, there are two types o checkout, self-checkout and regular checkout. By analyzing the data, Walmart can determine which type of checkout is the best for each store Third, to manage the supply chain processes, Walmart simulate the number of steps from the freight vessel to each store. This allows the company to optimize routes to the shipping dock and track the number of times a product gets touched along the way to the customer. The company uses data to analyze driving routes and transportation lanes for Walmart trucks as well.
Profiling, is a way of finding a killer or killers. A profile is created by using their psychological and behavioral characteristics. I learned a lot about this from the tv show called Criminal Minds, it is about a group of F.B.I. detectives that look beyond the evidence and look more on the psychology of the killer. They use this method to find the killer and save victims. Profiling is an actual method used by detectives in the F.B.I. not just the T.V. show.
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.
Classification is defined as the process of classifying something according to shared qualities or characteristics. Classification is used to help promote better organization skills. When a topic is organized the information regarding the subject can be easily located. For example, people enjoy different vacation spots such as; the beach, the mountains, and the lake.
Storing a set of names, and you look up on that on the basis of the prefix of name basically used in internet routers.
Data is collected and the patterns are recognized, in order to understand the physical properties, and further to visualize the data as
Customer Relationship Management (CRM) is another field where A.I. is used. There is no doubt that the internet has changed the way that businesses and corporations interact with their customers, and A.I. helps by offering a myriad of data about the customersuch as their demographics and purchasing history. A.I. offers analytics in real-time, greatly benefitting the company as it works to improve its marketing and ultimately its profits.
Mining frequent disease from medical data using association rule mining technique Abstract Health care industry today generates large amounts of complex data for patients, hospitals facilities, diseases, disease diagnostic methods, electronic patients records, etc .The data mining techniques are very useful to make medicinal decisions, specially to analyze the information about frequently occurring disease from large dataset obtained from hospital so that in future healthcare administrator will able to improve the quality of service. Until now some works done on this where they had collected medical data from particular area and improve the accuracy of classification, increase in the prediction of various diseases. But here in the proposed technique work will be done on mining the frequent disease of patients in different geographical area at given time period.
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.
9 Fayyad U., Piatetsky-Shapiro G., Smyth, Padhraic - "The KDD Process for Extracting Useful Knowledge from volumes of Data" - Communications of the ACM vol. 39, no. 11 (Nov. 1996).
Information privacy, or data privacy is the relationship between distribution of data, technology, the public expectation of privacy, and the legal and political issues surrounding them.