Web mining Wikipedia Le Data Mining, qu'est-ce que c'est ? The devil is in the details. C'est un outil d'exploration des donnГ©es dГ©cisionnelles DГ©finition: Le Data Mining est en fait un terme gГ©nГ©rique englobant toute une famille d'outils facilitant l'exploration et l'analyse des donnГ©es contenues au sein d'une base dГ©cisionnelle de type Data Warehouse ou DataMart.

## Five Data Mining Techniques That Help Create Business Value

Introduction to Data Mining exinfm. Each of the following data mining techniques cater to a different business problem and provides a different insight. Knowing the type of business problem that youвЂ™re trying to solve, will determine the type of data mining technique that will yield the best results., Sources:- data-mining.philippe-fournier. Regression Analysis is the best choice to perform prediction. It can be used to set a relationship between independent variables and dependent variables. 5. Sequential patterns or Pattern tracking: This data mining method is used to identify patterns that occur frequently over a certain period of time..

Sources:- data-mining.philippe-fournier. Regression Analysis is the best choice to perform prediction. It can be used to set a relationship between independent variables and dependent variables. 5. Sequential patterns or Pattern tracking: This data mining method is used to identify patterns that occur frequently over a certain period of time. Data mining is the process of looking at large banks of information to generate new information. Intuitively, you might think that data вЂњminingвЂќ refers to the extraction of new data, but this isnвЂ™t the case; instead, data mining is about extrapolating patterns and new knowledge from the data вЂ¦

data mining techniques. Overall, six broad classes of data mining algorithms are covered. Although there are a number of other algorithms and many variations of the techniques described, one of the algorithms from this group of six is almost always used in real world deployments of data mining systems. - Types of Data-Mining Algorithms.вЂ¦Classification.вЂ¦This is probably the most popular data-mining algorithm,вЂ¦simply because the results are very easy to understand.вЂ¦Decision trees, which are a type of classification,вЂ¦try to predict value of a column or columnsвЂ¦based on the relationshipsвЂ¦between the columns you have identified.вЂ¦Decision trees also determineвЂ¦which input columns

Categorical data: Categorical data represent characteristics such as a personвЂ™s gender, marital status, hometown, or the types of movies they like. Categorical data can take on numerical values (such as вЂњ1вЂќ indicating male and вЂњ2вЂќ indicating female), but those numbers donвЂ™t have mathematical meaning. Two different approaches were taken in initially deп¬Ѓning web mining. First was a вЂњprocess-centric view,вЂќ which deп¬Ѓned web mining as a sequence of tasks (Etzioni 1996). Second was a вЂњdata-centric view,вЂќ which deп¬Ѓned web mining in terms of the types of web data that was being used in the mining process (Cooley, Srivastava, and

Each of the following data mining techniques cater to a different business problem and provides a different insight. Knowing the type of business problem that youвЂ™re trying to solve, will determine the type of data mining technique that will yield the best results. The no-coupling data mining architecture does not take any advantages of a database. That is already very efficient in organizing, storing, accessing and retrieving data. The no-coupling architecture is considered a poor architecture for data mining system. But it is used for simple data mining processes.

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for Data Mining Tasks Prediction Tasks Use some variables to predict unknown or future values of other variables Description Tasks Find human-interpretable patterns that describe the data.Common data mining tasks Classification [Predictive] Clustering [Descriptive] Association Rule Discovery [Descriptive] Sequential Pattern Discovery [Descriptive

Two types of data mining tasks are there descriptive data mining tasks that describe the general properties of the existing data, and predictive data mining tasks that attempt to do predictions based on inference on available data. The data mining functionalities and the variety of knowledge they discover are briefly presented in the following list: Characterization: Data characterization is a We can specify a data mining task in the form of a data mining query. This query is input to the system. A data mining query is defined in terms of data mining task primitives.

Each of the following data mining techniques cater to a different business problem and provides a different insight. Knowing the type of business problem that youвЂ™re trying to solve, will determine the type of data mining technique that will yield the best results. Data mining is the process of looking at large banks of information to generate new information. Intuitively, you might think that data вЂњminingвЂќ refers to the extraction of new data, but this isnвЂ™t the case; instead, data mining is about extrapolating patterns and new knowledge from the data вЂ¦

1. Objective. In our last tutorial, we studied Data Mining Techniques.Today, we will learn Data Mining Algorithms. We will try to cover all types of Algorithms in Data Mining: Statistical Procedure Based Approach, Machine Learning Based Approach, Neural Network, Classification Algorithms in Data Mining, ID3 Algorithm, C4.5 Algorithm, K Nearest Neighbors Algorithm, NaГЇve Bayes Algorithm, SVM Data mining is a dynamic and fast-expanding п¬Ѓeld with great strengths. In this section, we brieп¬‚y outline the major issues in data mining research, partitioning them into п¬Ѓve groups: mining methodology, user interaction, efп¬Ѓciency and scalability, diversity of data types, and data mining and society. Many of these issues have been

In principle, data mining is not specific to one type of media or data. Data mining should be applicable to any kind of information repository. However, algorithms and approaches may differ when applied to different types of data. Indeed, the challenges presented by different types of data vary significantly. Data mining is being put into use Data Types are an important concept of statistics, which needs to be understood, to correctly apply statistical measurements to your data and therefore to correctly conclude certain assumptions about it. This blog post will introduce you to the different data types you need to know, to do proper exploratory data analysis (EDA), which is one of the most underestimated parts of a machine

This would involve the area of data mining known as anomaly de-tection. This could also be considered as a classiп¬Ѓcation problem if we had examples of both normal and abnormal heart behavior. (h) Monitoring seismic waves for earthquake activities. Yes. In this case, we would build a model of diп¬Ђerent types вЂ¦ effective data mining strategies. In fact, data mining in healthcare today remains, for the most part, an academic exercise with only a few pragmatic success stories. Academicians are using data-mining approaches like decision trees, clusters, neural networks, and time series to publish research.

### (PDF) Data Mining Concepts and Techniques.

Data Mining Processes Data Mining tutorial by Wideskills. diseases effective analysis of data mining is used [12-21]. Proposed a data mining methodology in order to improve the result [22-24] and proposed new data mining methodology [25, 26] and proposed framework in order to improved the healthcare system [27-31]. 2. Data Mining вЂ¦, In principle, data mining is not specific to one type of media or data. Data mining should be applicable to any kind of information repository. However, algorithms and approaches may differ when applied to different types of data. Indeed, the challenges presented by different types of data vary significantly. Data mining is being put into use.

Data Mining Processes Data Mining tutorial by Wideskills. Different data mining techniques can help organisations and scientists to find and select the most important and relevant information to create more value, This type of data mining technique refers to observation of data items in the dataset which do not match an expected pattern or expected behavior. This technique can be used in a variety of domains, such as intrusion, detection, fraud or fault detection, etc. Outer detection is also called Outlier Analysis or вЂ¦.

### Web Mining Data Analysis and Management Research Group

attributes types in data mining T4tutorials.com. Data mining is the process of looking at large banks of information to generate new information. Intuitively, you might think that data вЂњminingвЂќ refers to the extraction of new data, but this isnвЂ™t the case; instead, data mining is about extrapolating patterns and new knowledge from the data вЂ¦ https://fr.wikipedia.org/wiki/Weka_(informatique) 1. QuвЂ™est-ce que le Data Mining? Le Data Mining est un nouveau champ situГ© au croisement de la statistique et des technologies de lвЂ™information (bases de donnГ©es, intelligence artificielle, apprentissage etc.) dont le but est de dГ©couvrir des structures dans de vastes ensembles de donnГ©es. Deux types: modГЁles et В« patterns В».

• Data Mining Tutorial Process Techniques Tools EXAMPLES
• Web mining Wikipedia
• attributes types in data mining T4tutorials.com

• In this tutorial, we will learn about Attribute types in data mining. The attribute is the property of the object. The attribute represents different features of the object. Example of attribute InвЂ¦ data mining techniques. Overall, six broad classes of data mining algorithms are covered. Although there are a number of other algorithms and many variations of the techniques described, one of the algorithms from this group of six is almost always used in real world deployments of data mining systems.

Data Types are an important concept of statistics, which needs to be understood, to correctly apply statistical measurements to your data and therefore to correctly conclude certain assumptions about it. This blog post will introduce you to the different data types you need to know, to do proper exploratory data analysis (EDA), which is one of the most underestimated parts of a machine - Types of Data-Mining Algorithms.вЂ¦Classification.вЂ¦This is probably the most popular data-mining algorithm,вЂ¦simply because the results are very easy to understand.вЂ¦Decision trees, which are a type of classification,вЂ¦try to predict value of a column or columnsвЂ¦based on the relationshipsвЂ¦between the columns you have identified.вЂ¦Decision trees also determineвЂ¦which input columns

effective data mining strategies. In fact, data mining in healthcare today remains, for the most part, an academic exercise with only a few pragmatic success stories. Academicians are using data-mining approaches like decision trees, clusters, neural networks, and time series to publish research. data mining techniques. Overall, six broad classes of data mining algorithms are covered. Although there are a number of other algorithms and many variations of the techniques described, one of the algorithms from this group of six is almost always used in real world deployments of data mining systems.

Data Mining Tasks Prediction Tasks Use some variables to predict unknown or future values of other variables Description Tasks Find human-interpretable patterns that describe the data.Common data mining tasks Classification [Predictive] Clustering [Descriptive] Association Rule Discovery [Descriptive] Sequential Pattern Discovery [Descriptive This type of data mining technique refers to observation of data items in the dataset which do not match an expected pattern or expected behavior. This technique can be used in a variety of domains, such as intrusion, detection, fraud or fault detection, etc. Outer detection is also called Outlier Analysis or вЂ¦

Data Mining - Clustering Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 7 SE Master Course Introduction The whole process of data mining cannot be completed in a single step. In other words, you cannot get the required information from the large volumes of data as simple as that. It is a very complex process than we think involving a number of processes. The processes including data cleaning, data integration, data selection, data transformation, data mining,

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar This would involve the area of data mining known as anomaly de-tection. This could also be considered as a classiп¬Ѓcation problem if we had examples of both normal and abnormal heart behavior. (h) Monitoring seismic waves for earthquake activities. Yes. In this case, we would build a model of diп¬Ђerent types вЂ¦

Introduction The whole process of data mining cannot be completed in a single step. In other words, you cannot get the required information from the large volumes of data as simple as that. It is a very complex process than we think involving a number of processes. The processes including data cleaning, data integration, data selection, data transformation, data mining, INTRODUCTION TO MINING 1.1 MININGвЂ™S CONTRIBUTION TO CIVILIZATION Mining may well have been the second of humankindвЂ™s earliest endeavorsвЂ” granted that agriculture was the п¬Ѓrst. The two industries ranked together as the primary or basic industries of early civilization. Little has changed in the

Data mining consists of various techniques which can be used to make prediction and classifications, where this technique estimates the possibility that will occur in the future by looking at some DATA MINING CLASSIFICATION FABRICIO VOZNIKA LEONARDO VIANA INTRODUCTION Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe. The tendency is to keep increasing year after year. It is not hard to find databases with Terabytes of data in enterprises and research facilities. That is over 1,099,511,627,776 bytes of data. There is invaluable

Data Mining - Concepts and Techniques. Article (PDF Available) В· January 2002 with 19,303 Reads How we measure 'reads' A 'read' is counted each time someone views a publication summary (such as INTRODUCTION TO MINING 1.1 MININGвЂ™S CONTRIBUTION TO CIVILIZATION Mining may well have been the second of humankindвЂ™s earliest endeavorsвЂ” granted that agriculture was the п¬Ѓrst. The two industries ranked together as the primary or basic industries of early civilization. Little has changed in the

INTRODUCTION TO MINING 1.1 MININGвЂ™S CONTRIBUTION TO CIVILIZATION Mining may well have been the second of humankindвЂ™s earliest endeavorsвЂ” granted that agriculture was the п¬Ѓrst. The two industries ranked together as the primary or basic industries of early civilization. Little has changed in the Data Mining - Concepts and Techniques. Article (PDF Available) В· January 2002 with 19,303 Reads How we measure 'reads' A 'read' is counted each time someone views a publication summary (such as

11/06/2018В В· Types of Sources of Data in Data Mining In this post, we will discuss what are different sources of data that are used in data mining process. The data from multiple sources are integrated into a common source known as Data Warehouse . Different data mining techniques can help organisations and scientists to find and select the most important and relevant information to create more value

## Techniques of Data Mining In Healthcare A Review

Data Mining Tools Towards Data Science. Originally, вЂњdata miningвЂќ or вЂњdata dredgingвЂќ was a derogatory term referring to attempts to extract information that was not supported by the data. Section 1.2 illustrates the sort of errorsone can make by trying to extract what really isnвЂ™t in the data. Today, вЂњdata miningвЂќ has taken on a positive meaning. Now, statisticians view data mining as the construction of a statistical, 24/05/2018В В· Data Mining: Data mining in general terms means mining or digging deep into data which is in different forms to gain patterns, and to gain knowledge on that pattern.In the process of data mining, large data sets are first sorted, then patterns are identified and relationships are established to perform data analysis and solve problems..

### Five Data Mining Techniques That Help Create Business Value

An Overview of Data Mining Techniques. Web mining is the process of using data mining techniques and algorithms to extract information directly from the Web by extracting it from Web documents and services, Web content, hyperlinks and server logs. The goal of Web mining is to look for patterns in Web data by collecting and analyzing information in order to gain insight into trends,, Data Mining - Clustering Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 7 SE Master Course.

24/05/2018В В· Data Mining: Data mining in general terms means mining or digging deep into data which is in different forms to gain patterns, and to gain knowledge on that pattern.In the process of data mining, large data sets are first sorted, then patterns are identified and relationships are established to perform data analysis and solve problems. effective data mining strategies. In fact, data mining in healthcare today remains, for the most part, an academic exercise with only a few pragmatic success stories. Academicians are using data-mining approaches like decision trees, clusters, neural networks, and time series to publish research.

The concepts and techniques presented in this book focus on such data. Data mining can also be applied to other forms of data (e.g., data streams, ordered/sequence data, graph or networked data, spatial data, text data, multimedia data, and the WWW). We present an overview of such data in Section 1.3.4. 11/10/2019В В· Most importantly, data mining techniques aim to provide insight that allows for a better understanding of data and its essential features. Companies and organizations can employ many different types of data mining methods. While they may take a similar approach, all usually strive to meet different goals. The purpose of predictive data mining

INTRODUCTION TO MINING 1.1 MININGвЂ™S CONTRIBUTION TO CIVILIZATION Mining may well have been the second of humankindвЂ™s earliest endeavorsвЂ” granted that agriculture was the п¬Ѓrst. The two industries ranked together as the primary or basic industries of early civilization. Little has changed in the Sources:- data-mining.philippe-fournier. Regression Analysis is the best choice to perform prediction. It can be used to set a relationship between independent variables and dependent variables. 5. Sequential patterns or Pattern tracking: This data mining method is used to identify patterns that occur frequently over a certain period of time.

Web mining is the process of using data mining techniques and algorithms to extract information directly from the Web by extracting it from Web documents and services, Web content, hyperlinks and server logs. The goal of Web mining is to look for patterns in Web data by collecting and analyzing information in order to gain insight into trends, Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for

Python users playing around with data sciences might be familiar with Orange. It is a Python library that powers Python scripts with its rich compilation of mining and machine learning algorithms for data pre-processing, classification, modelling, regression, clustering and other miscellaneous functions. data mining techniques. Overall, six broad classes of data mining algorithms are covered. Although there are a number of other algorithms and many variations of the techniques described, one of the algorithms from this group of six is almost always used in real world deployments of data mining systems.

Data mining is the process of looking at large banks of information to generate new information. Intuitively, you might think that data вЂњminingвЂќ refers to the extraction of new data, but this isnвЂ™t the case; instead, data mining is about extrapolating patterns and new knowledge from the data вЂ¦ This would involve the area of data mining known as anomaly de-tection. This could also be considered as a classiп¬Ѓcation problem if we had examples of both normal and abnormal heart behavior. (h) Monitoring seismic waves for earthquake activities. Yes. In this case, we would build a model of diп¬Ђerent types вЂ¦

logs). Web data mining is a sub discipline of data mining which mainly deals with web. Web data mining is divided into three different types: web structure, web content and web usage mining. All these types use different techniques, tools, approaches, algorithms for discover information from huge bulks of data вЂ¦ Python users playing around with data sciences might be familiar with Orange. It is a Python library that powers Python scripts with its rich compilation of mining and machine learning algorithms for data pre-processing, classification, modelling, regression, clustering and other miscellaneous functions.

effective data mining strategies. In fact, data mining in healthcare today remains, for the most part, an academic exercise with only a few pragmatic success stories. Academicians are using data-mining approaches like decision trees, clusters, neural networks, and time series to publish research. Data mining is the process of looking at large banks of information to generate new information. Intuitively, you might think that data вЂњminingвЂќ refers to the extraction of new data, but this isnвЂ™t the case; instead, data mining is about extrapolating patterns and new knowledge from the data вЂ¦

Data mining can be conducted on any kind of data as long as the data are meaningful for a target application, such as database data, data warehouse data, transactional data, and advanced data types. Finally major data mining research and development issues are outlined. Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for

### What is Web Mining? Definition from Techopedia

Classification of Data Mining Systems BrainKart. Given below is a list of Top Data Mining Algorithms: 1. C4.5: C4.5 is an algorithm that is used to generate a classifier in the form of a decision tree and has been developed by Ross Quinlan. And in order to do the same, C4.5 is given a set of data that represent things that have already been classified., Data mining is a dynamic and fast-expanding п¬Ѓeld with great strengths. In this section, we brieп¬‚y outline the major issues in data mining research, partitioning them into п¬Ѓve groups: mining methodology, user interaction, efп¬Ѓciency and scalability, diversity of data types, and data mining and society. Many of these issues have been.

### Types of data-mining algorithms lynda.com

Data Mining Methods Top 8 Types Of Data Mining Method. Different data mining techniques can help organisations and scientists to find and select the most important and relevant information to create more value https://en.wikipedia.org/wiki/Wikipedia:Data_mining_Wikipedia Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for.

• Data Mining Architecture Data Mining Types and
• (PDF) DATA MINING TECHNIQUES AND APPLICATIONS
• Introduction to Data Mining exinfm

• Data mining can be conducted on any kind of data as long as the data are meaningful for a target application, such as database data, data warehouse data, transactional data, and advanced data types. Finally major data mining research and development issues are outlined. INTRODUCTION TO MINING 1.1 MININGвЂ™S CONTRIBUTION TO CIVILIZATION Mining may well have been the second of humankindвЂ™s earliest endeavorsвЂ” granted that agriculture was the п¬Ѓrst. The two industries ranked together as the primary or basic industries of early civilization. Little has changed in the

11/06/2018В В· Types of Sources of Data in Data Mining In this post, we will discuss what are different sources of data that are used in data mining process. The data from multiple sources are integrated into a common source known as Data Warehouse . 14/10/2019В В· Data mining analysis can be a useful process that provides different results depending on the specific algorithm used for data evaluation. Common types of data mining analysis include exploratory data analysis (EDA), descriptive modeling, predictive modeling and discovering patterns and rules. Utilization of each of these data mining tools provides a different perspective on collected вЂ¦

The no-coupling data mining architecture does not take any advantages of a database. That is already very efficient in organizing, storing, accessing and retrieving data. The no-coupling architecture is considered a poor architecture for data mining system. But it is used for simple data mining processes. DATA MINING CLASSIFICATION FABRICIO VOZNIKA LEONARDO VIANA INTRODUCTION Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe. The tendency is to keep increasing year after year. It is not hard to find databases with Terabytes of data in enterprises and research facilities. That is over 1,099,511,627,776 bytes of data. There is invaluable

In principle, data mining is not specific to one type of media or data. Data mining should be applicable to any kind of information repository. However, algorithms and approaches may differ when applied to different types of data. Indeed, the challenges presented by different types of data vary significantly. Data mining is being put into use 14/10/2019В В· Data mining analysis can be a useful process that provides different results depending on the specific algorithm used for data evaluation. Common types of data mining analysis include exploratory data analysis (EDA), descriptive modeling, predictive modeling and discovering patterns and rules. Utilization of each of these data mining tools provides a different perspective on collected вЂ¦

- Types of Data-Mining Algorithms.вЂ¦Classification.вЂ¦This is probably the most popular data-mining algorithm,вЂ¦simply because the results are very easy to understand.вЂ¦Decision trees, which are a type of classification,вЂ¦try to predict value of a column or columnsвЂ¦based on the relationshipsвЂ¦between the columns you have identified.вЂ¦Decision trees also determineвЂ¦which input columns Le Data Mining, qu'est-ce que c'est ? The devil is in the details. C'est un outil d'exploration des donnГ©es dГ©cisionnelles DГ©finition: Le Data Mining est en fait un terme gГ©nГ©rique englobant toute une famille d'outils facilitant l'exploration et l'analyse des donnГ©es contenues au sein d'une base dГ©cisionnelle de type Data Warehouse ou DataMart.

11/06/2018В В· Types of Sources of Data in Data Mining In this post, we will discuss what are different sources of data that are used in data mining process. The data from multiple sources are integrated into a common source known as Data Warehouse . Two different approaches were taken in initially deп¬Ѓning web mining. First was a вЂњprocess-centric view,вЂќ which deп¬Ѓned web mining as a sequence of tasks (Etzioni 1996). Second was a вЂњdata-centric view,вЂќ which deп¬Ѓned web mining in terms of the types of web data that was being used in the mining process (Cooley, Srivastava, and

Categorical data: Categorical data represent characteristics such as a personвЂ™s gender, marital status, hometown, or the types of movies they like. Categorical data can take on numerical values (such as вЂњ1вЂќ indicating male and вЂњ2вЂќ indicating female), but those numbers donвЂ™t have mathematical meaning. Data Mining - Clustering Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 7 SE Master Course

This would involve the area of data mining known as anomaly de-tection. This could also be considered as a classiп¬Ѓcation problem if we had examples of both normal and abnormal heart behavior. (h) Monitoring seismic waves for earthquake activities. Yes. In this case, we would build a model of diп¬Ђerent types вЂ¦ DATA MINING CLASSIFICATION FABRICIO VOZNIKA LEONARDO VIANA INTRODUCTION Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe. The tendency is to keep increasing year after year. It is not hard to find databases with Terabytes of data in enterprises and research facilities. That is over 1,099,511,627,776 bytes of data. There is invaluable

Originally, вЂњdata miningвЂќ or вЂњdata dredgingвЂќ was a derogatory term referring to attempts to extract information that was not supported by the data. Section 1.2 illustrates the sort of errorsone can make by trying to extract what really isnвЂ™t in the data. Today, вЂњdata miningвЂќ has taken on a positive meaning. Now, statisticians view data mining as the construction of a statistical Data mining is a dynamic and fast-expanding п¬Ѓeld with great strengths. In this section, we brieп¬‚y outline the major issues in data mining research, partitioning them into п¬Ѓve groups: mining methodology, user interaction, efп¬Ѓciency and scalability, diversity of data types, and data mining and society. Many of these issues have been

Categorical data: Categorical data represent characteristics such as a personвЂ™s gender, marital status, hometown, or the types of movies they like. Categorical data can take on numerical values (such as вЂњ1вЂќ indicating male and вЂњ2вЂќ indicating female), but those numbers donвЂ™t have mathematical meaning. Web mining is the process of using data mining techniques and algorithms to extract information directly from the Web by extracting it from Web documents and services, Web content, hyperlinks and server logs. The goal of Web mining is to look for patterns in Web data by collecting and analyzing information in order to gain insight into trends,

Originally, вЂњdata miningвЂќ or вЂњdata dredgingвЂќ was a derogatory term referring to attempts to extract information that was not supported by the data. Section 1.2 illustrates the sort of errorsone can make by trying to extract what really isnвЂ™t in the data. Today, вЂњdata miningвЂќ has taken on a positive meaning. Now, statisticians view data mining as the construction of a statistical Originally, вЂњdata miningвЂќ or вЂњdata dredgingвЂќ was a derogatory term referring to attempts to extract information that was not supported by the data. Section 1.2 illustrates the sort of errorsone can make by trying to extract what really isnвЂ™t in the data. Today, вЂњdata miningвЂќ has taken on a positive meaning. Now, statisticians view data mining as the construction of a statistical