A Review of Data Mining Techniques
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paper_title: Predictive Data Mining: A Practical Guide

paper_content:

1 What is Data Mining? 2 Statistical Evaluation for Big Data 3 Preparing the Data 4 Data Reduction 5 Looking for Solutions 6 What's Best for Data Reduction and Mining? 7 Art or Science? Case Studies in Data Mining
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paper_title: Knowledge Discovery in Databases

paper_content:

From the Publisher: ::: Knowledge Discovery in Databases brings together current research on the exciting problem of discovering useful and interesting knowledge in databases. It spans many different approaches to discovery, including inductive learning, bayesian statistics, semantic query optimization, knowledge acquisition for expert systems, information theory, and fuzzy 1 sets. ::: The rapid growth in the number and size of databases creates a need for tools and techniques for intelligent data understanding. Relationships and patterns in data may enable a manufacturer to discover the cause of a persistent disk failure or the reason for consumer complaints. But today's databases hide their secrets beneath a cover of overwhelming detail. The task of uncovering these secrets is called "discovery in databases." This loosely defined subfield of machine learning is concerned with discovery from large amounts of possible uncertain data. Its techniques range from statistics to the use of domain knowledge to control search. ::: Following an overview of knowledge discovery in databases, thirty technical chapters are grouped in seven parts which cover discovery of quantitative laws, discovery of qualitative laws, using knowledge in discovery, data summarization, domain specific discovery methods, integrated and multi-paradigm systems, and methodology and application issues. An important thread running through the collection is reliance on domain knowledge, starting with general methods and progressing to specialized methods where domain knowledge is built in. ::: Gregory Piatetski-Shapiro is Senior Member of Technical Staff and Principal Investigator of the Knowledge Discovery Project at GTELaboratories. William Frawley is Principal Member of Technical Staff at GTE and Principal Investigator of the Learning in Expert Domains Project.
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paper_title: Data Mining: An Overview from a Database Perspective

paper_content:

Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many di erent elds have shown great interest in data mining. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classi cation of the available data mining techniques is provided and a comparative study of such techniques is presented. Index Terms | Data mining, knowledge discovery, association rules, classi cation, data clustering, pattern matching algorithms, data generalization and characterization, data cubes, multiple-dimensional databases. J. Han was supported in part by the research grant NSERC-A3723 from the Natural Sciences and Engineering Research Council of Canada, the research grant NCE:IRIS/Precarn-HMI5 from the Networks of Centres of Excellence of Canada, and research grants from MPR Teltech Ltd. and Hughes Research Laboratories.
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paper_title: Data Mining: An Overview from a Database Perspective

paper_content:

Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many di erent elds have shown great interest in data mining. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classi cation of the available data mining techniques is provided and a comparative study of such techniques is presented. Index Terms | Data mining, knowledge discovery, association rules, classi cation, data clustering, pattern matching algorithms, data generalization and characterization, data cubes, multiple-dimensional databases. J. Han was supported in part by the research grant NSERC-A3723 from the Natural Sciences and Engineering Research Council of Canada, the research grant NCE:IRIS/Precarn-HMI5 from the Networks of Centres of Excellence of Canada, and research grants from MPR Teltech Ltd. and Hughes Research Laboratories.
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paper_title: Mining association rules between sets of items in large databases

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We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
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paper_title: The World-Wide Web: quagmire or gold mine?

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An efficient continuous wave solid state laser is described. An array of semiconductor diode lasers is employed to generate a pump light source at an absorption wavelength for a solid state laser such as Nd:YAG laser. The array of diode lasers is pulsed at a high repetition rate to produce narrow light pulses which are directed upon an end surface of the solid state laser rod. The pump pulses rate is selected sufficiently high to establish CW operation of the solid state laser while the duty cycle for the pump-light source is sufficiently low to avoid excessive junction heating of the diode lasers, which would substantially reduce their output power.
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paper_title: MINE OVER MATTER

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There are obscene amounts of data in corporate coffers that could be used to reinvent marketing strategies. Data mining is one way to find the information that counts.
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Title: A Review of Data Mining Techniques

Section 1: Introduction
Description 1: Provide an introduction to data mining, its importance, and its role in extracting useful information from large datasets.

Section 2: Current trends on data mining
Description 2: Discuss the recent trends and developments in data mining, including the growing interest in the field and its applications in various industries.

Section 3: Requirements and challenges of DM
Description 3: Outline the main requirements and challenges associated with data mining, including handling different types of data, algorithm efficiency, and data security.

Section 4: Data mining steps
Description 4: Describe the general steps involved in the data mining process, including data preparation, data reduction, and information extraction.

Section 5: Classifying DM techniques
Description 5: Explain how data mining techniques can be classified based on the type of database, the knowledge to be discovered, and the techniques employed.

Section 6: Major DM techniques
Description 6: Review and discuss the major data mining techniques, including statistics, transactional/relational database mining, AI techniques, decision trees, genetic algorithms, and visualization.

Section 7: Conclusion
Description 7: Summarize the key points discussed in the paper and highlight the future prospects and unresolved issues in the field of data mining.