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Applications And Trends In Data Mining Pdf

applications and trends in data mining pdf

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Data mining is a method of extracting data from multiple sources and organizing it to derive valuable insights. Read on to discover the wide-ranging data mining applications that are changing the industry as we know it! Modern-day companies cannot live in a data lacuna. They have to evolve and keep up with technological evolution and upcoming digital trends to stay ahead of the competition.

Data Mining Application And Trends In Data Mining:

Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning , statistics , and database systems. The term "data mining" is a misnomer , because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction mining of data itself. The book Data mining: Practical machine learning tools and techniques with Java [8] which covers mostly machine learning material was originally to be named just Practical machine learning , and the term data mining was only added for marketing reasons.

The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records cluster analysis , unusual records anomaly detection , and dependencies association rule mining , sequential pattern mining. This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics.

For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.

The related terms data dredging , data fishing , and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are or may be too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.

In the s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. The term "data mining" was used in a similarly critical way by economist Michael Lovell in an article published in the Review of Economic Studies in The term data mining appeared around in the database community, generally with positive connotations.

Other terms used include data archaeology , information harvesting , information discovery , knowledge extraction , etc. Gregory Piatetsky-Shapiro coined the term "knowledge discovery in databases" for the first workshop on the same topic KDD and this term became more popular in AI and machine learning community.

However, the term data mining became more popular in the business and press communities. It was co-chaired by Usama Fayyad and Ramasamy Uthurusamy. A year later, in , Usama Fayyad launched the journal by Kluwer called Data Mining and Knowledge Discovery as its founding editor-in-chief. The journal Data Mining and Knowledge Discovery is the primary research journal of the field. The manual extraction of patterns from data has occurred for centuries.

Early methods of identifying patterns in data include Bayes' theorem s and regression analysis s. The proliferation, ubiquity and increasing power of computer technology have dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, specially in the field of machine learning, such as neural networks , cluster analysis , genetic algorithms s , decision trees and decision rules s , and support vector machines s.

Data mining is the process of applying these methods with the intention of uncovering hidden patterns. It bridges the gap from applied statistics and artificial intelligence which usually provide the mathematical background to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever-larger data sets.

Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit.

A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining.

The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data. Data mining involves six common classes of tasks: [5]. Data mining can unintentionally be misused, and can then produce results that appear to be significant; but which do not actually predict future behavior and cannot be reproduced on a new sample of data and bear little use.

Often this results from investigating too many hypotheses and not performing proper statistical hypothesis testing. The final step of knowledge discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set.

Not all patterns found by data mining algorithms are necessarily valid. It is common for data mining algorithms to find patterns in the training set which are not present in the general data set.

This is called overfitting. To overcome this, the evaluation uses a test set of data on which the data mining algorithm was not trained. The learned patterns are applied to this test set, and the resulting output is compared to the desired output. For example, a data mining algorithm trying to distinguish "spam" from "legitimate" emails would be trained on a training set of sample e-mails.

Once trained, the learned patterns would be applied to the test set of e-mails on which it had not been trained. The accuracy of the patterns can then be measured from how many e-mails they correctly classify. Several statistical methods may be used to evaluate the algorithm, such as ROC curves.

If the learned patterns do not meet the desired standards, subsequently it is necessary to re-evaluate and change the pre-processing and data mining steps. If the learned patterns do meet the desired standards, then the final step is to interpret the learned patterns and turn them into knowledge.

JDM 2. As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications. However, extensions to cover for example subspace clustering have been proposed independently of the DMG. Data mining is used wherever there is digital data available today. Notable examples of data mining can be found throughout business, medicine, science, and surveillance.

While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to peoples' behavior ethical and otherwise. The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy , legality, and ethics.

Data mining requires data preparation which uncovers information or patterns which compromise confidentiality and privacy obligations. A common way for this to occur is through data aggregation. Data aggregation involves combining data together possibly from various sources in a way that facilitates analysis but that also might make identification of private, individual-level data deducible or otherwise apparent.

The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when the data were originally anonymous. It is recommended [ according to whom? Data may also be modified so as to become anonymous, so that individuals may not readily be identified. The inadvertent revelation of personally identifiable information leading to the provider violates Fair Information Practices.

This indiscretion can cause financial, emotional, or bodily harm to the indicated individual. In one instance of privacy violation , the patrons of Walgreens filed a lawsuit against the company in for selling prescription information to data mining companies who in turn provided the data to pharmaceutical companies. Europe has rather strong privacy laws, and efforts are underway to further strengthen the rights of the consumers.

However, the U. Safe Harbor Principles , developed between and , currently effectively expose European users to privacy exploitation by U. As a consequence of Edward Snowden 's global surveillance disclosure , there has been increased discussion to revoke this agreement, as in particular the data will be fully exposed to the National Security Agency , and attempts to reach an agreement with the United States have failed.

In the United Kingdom in particular there have been cases of corporations using data mining as a way to target certain groups of customers forcing them to pay unfairly high prices. These groups tend to be people of lower socio-economic status who are not savvy to the ways they can be exploited in digital market places. The HIPAA requires individuals to give their "informed consent" regarding information they provide and its intended present and future uses.

More importantly, the rule's goal of protection through informed consent is approach a level of incomprehensibility to average individuals.

The use of data mining by the majority of businesses in the U. Under European copyright and database laws , the mining of in-copyright works such as by web mining without the permission of the copyright owner is not legal. Where a database is pure data in Europe, it may be that there is no copyright—but database rights may exist so data mining becomes subject to intellectual property owners' rights that are protected by the Database Directive.

On the recommendation of the Hargreaves review , this led to the UK government to amend its copyright law in to allow content mining as a limitation and exception. However, due to the restriction of the Information Society Directive , the UK exception only allows content mining for non-commercial purposes.

UK copyright law also does not allow this provision to be overridden by contractual terms and conditions. The European Commission facilitated stakeholder discussion on text and data mining in , under the title of Licences for Europe. US copyright law , and in particular its provision for fair use , upholds the legality of content mining in America, and other fair use countries such as Israel, Taiwan and South Korea.

As content mining is transformative, that is it does not supplant the original work, it is viewed as being lawful under fair use. For example, as part of the Google Book settlement the presiding judge on the case ruled that Google's digitization project of in-copyright books was lawful, in part because of the transformative uses that the digitization project displayed—one being text and data mining. Public access to application source code is also available. For more information about extracting information out of data as opposed to analyzing data , see:.

From Wikipedia, the free encyclopedia. Finding patterns in large data sets using complex computational methods. Dimensionality reduction. Structured prediction. Graphical models Bayes net Conditional random field Hidden Markov.

Anomaly detection. Artificial neural network. Reinforcement learning. Machine-learning venues. Glossary of artificial intelligence. Related articles. List of datasets for machine-learning research Outline of machine learning. This section is missing information about non-classification tasks in data mining. It only covers machine learning. Please expand the section to include this information.

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Data mining

Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning , statistics , and database systems. The term "data mining" is a misnomer , because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction mining of data itself. The book Data mining: Practical machine learning tools and techniques with Java [8] which covers mostly machine learning material was originally to be named just Practical machine learning , and the term data mining was only added for marketing reasons. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records cluster analysis , unusual records anomaly detection , and dependencies association rule mining , sequential pattern mining. This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics.

Activities in data warehousing and mining are constantly emerging. Data mining methods, algorithms, online analytical processes, data mart and practical issues consistently evolve, providing a challenge for professionals in the field. Research and Trends in Data Mining Technologies and Applications focuses on the integration between the fields of data warehousing and data mining, with emphasis on the applicability to real-world problems. This book provides an international perspective, highlighting solutions to some of researchers' toughest challenges. Developments in the knowledge discovery process, data models, structures, and design serve as answers and solutions to these emerging challenges.


PDF | On Jun 6, , Mohit Saini published DATA MINING TREND IN the several challenges, the current trends of data mining applications.


5 Important Future Trends in Data Mining

Data Mining - Applications & Trends

Data mining is one of the most widely used methods to extract data from different sources and organize them for better usage. In spite of having different commercial systems for data mining, a lot of challenges come up when they are actually implemented. With rapid evolution in the field of data mining, companies are expected to stay abreast with all the new developments. Complex algorithms form the basis for data mining as they allow for data segmentation to identify various trends and patterns, detect variations, and predict the probabilities of various events happening.

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2 Comments

  1. Fiorel S.

    12.04.2021 at 03:51
    Reply

    Data Mining has its great application in Retail Industry because it collects large amount data from on sales, customer purchasing history, goods transportation.

  2. Aladino C.

    12.04.2021 at 07:29
    Reply

    DM researchers and data mining system and application developers. The era of data mining applications was conceived in the year

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