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Data mining refers to the study of data (usually by software without human intervention) that is generated by user behavious on the internet. For example, a visit to Amazon.com and a look at books on anthropology will probably trigger amazon's software to flag one as someone interested in anthropolgy. This data is then used across the user's net experience on sites like Facebook, Game sites, etc., to show the user ads related to resources on anthropology. This is done through the use of "cookies" that are placed on the user's computer that can then be read by sites that partner with the cookie-placing site to show relevant ads.
While data mining is the first step in collecting user data, showing ads related to what the user was browsing can be redundant. Going by the previous example, if a user has already bought a book on basic anthropology, it makes little sense to show ads for the very same book. Predictive modeling goes a step or two further. Given that the user has already bought a book on basic anthropology, predictive modeling seeks to predict what the user will most likely need next and then to show ads for those products or services.
Predictive modeling uses much more of the data mined, such as the user's age, gender, known experience in the field, other related interests, etc., to build a model to predict future needs and to thus show ads tailored to those needs.
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What is predictive data mining?
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Where can aspiring models find predictive modeling blogs?
There are many places where aspiring models can find predictive modeling blogs. Aspiring models can find predictive modeling blogs at popular on the web sources such as Blogger, Enservio, and Blogspot.
What is predictive analytics and how is it useful?
Predictive analytics is used to predict client responses and purchases, as well as cross-sell opportunities. Businesses can use predictive models to acquire, keep, and expand their most profitable consumers. Operations are being improved. Predictive models are used by many businesses to forecast inventory and manage resources. To learn more about data science please visit- Learnbay.co
What is predictive modeling?
Predictive Modelling is made up of predictors which are changeable factors that are likely to influence future results.
What is the definition of predictive analytics?
Predictive analytics is a way of using data from various sources, such as data mining and gaming in order to predict future events. Also, current and historical events are taken into consideration. Insurance companies use predictive analytics when issuing coverage for automobiles. Predicting future trends is another use for predictive analytics.
Characteristics of data mining?
CHARECTERISTICS OF DATA MINING CHARECTERISTICS OF DATA MINING
Distinguish between Data mining and text mining?
mining the data is called data mining. Mining the text is called text mining
Different types of data mining?
spatial data mining time series data mining text or multimedia data mining www mining systems
What are the techniques of data mining?
Data mining is one part of the process of Knowledge Discovery in Databases. There are many techniques within data mining that aim to accomplish different tasks. Generally tasks fall into one of two categories, predictive or descriptive. Predictive tasks look at historical data to predict what will happen in the future. Descriptive tasks will look at some given data and find patterns in it. Since data mining is a growing area, the techniques are constantly changing, as new improved methods are discovered. At present, some of the most well known predictive algorithms, known as classification algorithms include Naive Bayes, SVM, Decision Trees (such as C4.5), Artificial Neural Networks, k-Nearest Neighbour and more. Some predictive algorithms are able to perform regression, a form of prediction for non-categorical data. Some of the most well known descriptive algorithms include the Apriori and FP-tree algorithms (for finding association rules), K-Means and Hierarchical clustering algorithms, GSP and PrefixSpan for Sequential Pattern Mining and various algorithms for Outlier Detection. In 2006, at the International Conference on Data Mining (ICDM), the top algorithms were discussed (see http://www.cs.uvm.edu/~icdm/algorithms/index.shtml). This is a very limited list and many more algorithms have been and are being developed, as this area continues to grow and expand to encompass new problems and applications.
Data mining in healthcare?
Data mining is increasingly popular in health care. It can help insurers find fraud and abuse, as well as help patients receive better, affordable healthcare. Data mining in health care helps organizations to make management decisions, and can help physicians identify the best treatments. It can also help health care providers to build a pattern that can be turned into a predictive model for the future.
what is Data Mining?
AnswerWhat is data mining?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 one of a number of 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 dozens of fields in large relational databasesData Mining is a field of study within Computer Science. It is part of the process of Knowledge Discovery from Databases (KDD). The aim of data mining is to find novel, interesting and useful patterns from data using algorithms (methods of finding such information) that will do it in a way that is more computationally efficient than previous methods.Knowledge Discovery and Data Mining has increased in popularity because of the large amount of stored data that came about as computer storage became cheaper. From this, there was a need to understand it, and techniques to convert data into information are being continually developed and improved.Data mining techniques usually fall into two categories, predictive or descriptive. Predictive data mining uses historical data to infer something about future events. Descriptive data mining aims to find patterns in the data that provide some information about what the data contains.How can data mining affect you?Data mining can be used for several purposes by different people and organisations. The most notable users of data mining come from commercial, scientific or government backgrounds.Commercial entities may use the information gathered through data mining techniques to help discover something about their consumers, to help market their products better. Data mining is also used by search engines, such as Google to mine web pages for information relating to your specific search query.Scientific communities may benefit from data mining by using it to find anomalies, clusters or co-locations to name a few. For example, they could discover a relationship between people getting cancer and the location of a chemical plant.The government could use data mining techniques to uncover patterns in their data. For example, data mining is used to find unusual patterns in the stock marketin order to detect insider trading. Data mining is also used to detect scams sent by email. It could also be used to find unusual behaviour to prevent a terrorist attack.There are many more applications of data mining, which are continually being expanded. The main requirement for performing data mining is suitable data.
What services do the data mining company provide?
Data Mining companies provide such services as mining for data and mining for data two electric bugaloo. They will often offer to resort to underhanded tactics to mine said data.
