The following Algorithm will be used for this.
Mining Model Algorithm
Data mining is a set of sophisticated tool and algorithms that will allow analyst and end users to solve the problems or else which would take huge amounts of manual effects or else would simply remain unsolved. Data mining algorithm are the foundations for creating the mining models. Algorithms are mathematical functions that will perform specific types of analysis on the associate data sets. SQL Server 2005 has seven world-class data mining algorithms. They are Microsoft Naïve Bayes, Microsoft Decision Trees, Microsoft Time Series, Microsoft Clustering, Microsoft Association Rules, Microsoft Neural Network and Text Mining (Zhaohui Tang and Jamine Maclennan, 2005). In these algorithms some are unsupervised and supervised. The supervised are Microsoft Association Rules, Microsoft Naïve Bayes, Microsoft Decision and Microsoft Neural Network. The unsupervised are Microsoft Time Series, Microsoft Clustering and Text Mining (Lynn Langit, 2007). Hence from the above context it can be understood that data mining is a set of sophisticated tool. They are seven data mining algorithm used in SQL Server 2005.
Microsoft Clustering algorithm finds the natural grouping inside the data when these grouping are not apparent. This will find the hidden variables that will accurately classified the data. This will find the hidden dimensions that are unique data, it will also provide the information in the way that is impossible to achieve with the predefined organizational methods. This algorithm uses iterative techniques to group records from the dataset into clusters which will contain similar characteristics (Lynn Langit, 2007). These types of algorithms are often used as a starting point to help end users to understand the relationship between attributes in a large volume of data in a better manner. These clusters can be used for explore the data, learning more about the relationships that exist, which may not be easy to derive logically through casual observation (Ray Rankins, Paul Jensen and Paul Bertucci, 2002). Hence it can be understood that Microsoft Clustering algorithm is find the hidden variables that will accurately classified the data.
Microsoft Association algorithm is related to priori association family. It is very efficient and popular algorithm to find frequent itemsets in the dataset. Two steps are involved in this algorithm in that first step is calculation intensive phase to find frequent itemsets and second one is create association rules based on the itemsets (Zhaohui Tang and Jamine Maclennan, 2005). This algorithm considered each value or attribute as an item. This is mainly developed to implement in the basket analysis. This algorithm makes the rules that explain which items are close to each other in the transformation (Mike Gunderloy and Joseph L.Jorden, 2006). It can find group of items called as itemsets in a single transformation. This algorithm search complete data set to discover item sets that tend to appear in many transactions. This algorithm contains parameters. The parameter SUPPORT defines how many transactions the itemsets must appear in before it is considered significant (Lynn Langit, 2007). Hence it can be understood that this algorithm is related to priori associate family. They are steps involved in this. The first step is calculation intensive phase and second is creating create association rules based.
Microsoft Naive Bayes
The Microsoft Naive Bayes algorithm will enable the user to quickly create models which will be having predictive abilities and also provides a new method of exploring and understanding user data. It will build the mining models that will be used for classifying and prediction. This algorithm helps in calculating the probabilities for each possible state of the input attribute. When each state of the predictable attribute is given, which can be used later to predict an outcome of the predicted attribute based on the known input attributes (Jamie Maclennan, Zhaohui Tang and Bogdan Crivat, 2009). This algorithm will support only the discrete or discredited attributes. In this all the input attributes are considered as independent. This is called naïve because they will be no one attribute that has higher significance. It is considered as a start point data mining process, because most of the calculations are used to create the model are generated during cube processing, results are retuned quickly (Lynn Langit, 2007). Hence from the above context it can be understood that Microsoft Naïve Bayes will help the user to create models quickly which will be having predictive abilities.
Microsoft Time Series
Microsoft time series algorithm is an algorithm which is used to predicting and analyzes the time dependent data. Generally, this algorithm is the combination of two algorithms in one industry standard ARIMA algorithm, which was which was introduced by Box and Jenkins and second algorithm is ARTxp algorithm developed by Microsoft (Brian Larson, 2008). Time series algorithm includes series of data gathered over successive periods of time or other time indicators. The main aim of this algorithm is to estimate the future series points and take the valuable decisions based on past historical data. This algorithm can produce best results with minimum of information (Jamie Maclennan, Zhaohui Tang and Bogdan Crivat, 2009). This algorithm has a great future that is it can automatically detect the seasonality with the help of fast Fourier transform so it is an efficient method to analyze the frequencies. One or more variables can be selected to predict by using this algorithm. It can use cross-variable correlations in its predictions (Zhaohui Tang and Jamine Maclennan, 2005). Hence, The Microsoft Time Series algorithm creates models that can be used to predict continuous variables over time from both OLAP and relational data sources.
Microsoft Sequence Clustering
Microsoft sequence clustering algorithm mainly used to analyze sequence data but it also many other uses. Segmentation and sequence analysis are the fundamental features of this algorithm. It can also used for classification and regression (Lynn Langit, 2007). The Microsoft Sequence Clustering algorithm is a hybrid of sequence and clustering algorithms. The algorithm groups multiple cases with sequence attributes into segments based on similarities of these sequences (Otey, 2005). The Microsoft Sequence Clustering algorithm can group these Web customers into more-or-less homogenous groups based on their navigations patterns. These groups can then be visualized, providing a detailed understanding of how customers are using the site (Florent Masseglia, Pascal Poncelet and Maguelonne Teisseire, 2007). Hence, form the above discussion it can be understood that it can analyzes sequence-oriented information that includes discrete-valued series. Usually the sequence attribute in the series holds a set of events with a specific order. By analyzing or predicting the transition between states of the sequence, the algorithm can predict future states in related sequences.
Microsoft Neural Network
The Microsoft Neural Network algorithm that will create a classification and regression mining models that can be constructed multilayer perceptron network of neurons. This Neural network technology can be applied to more and more commercial applications. This uses the weighted sum approach in this the output of combination is then passed through the activation function. The Microsoft Neural Network works by creating and training artificial neural paths that are used as patterns for further prediction (Jamie Maclennan, Zhaohui Tang and Bogdan Crivat, 2009). The Microsoft Neural Network is used as a Discrimination Viewer similar to those the other algorithm. This algorithm will provide processes the entire set of cases, iterating comparing the predicted classification of the cases with the known actual classification of the cases. Neural networks are more complicated than Naïve Bayes and decision trees (Zhaohui Tang and Jamine Maclennan, 2005). Thus, when the clients need to apply the algorithm in more than one application this is the best algorithm technique.
Microsoft Logistic Regression
Microsoft Logistic regression algorithm is another form of Microsoft Neural Network algorithm. Logistic regression is a well-known statistical method for determining the contribution of multiple factors to a pair of outcomes (Msdn, 2009). If the problem contains one of two possible outcomes this algorithm is very useful to model that data. This algorithm can be used in many fields because of its flexibility (Brian Larson, 2008). This algorithm has been mostly used by statisticians to predict and model the statistical and probability information based on input values. This algorithm can support the prediction of both continuous and discrete attributes (Jamie Maclennan, Zhaohui Tang and Bogdan Crivat, 2009). Hence, from the above discussion it can be understood that Logistic regression algorithm is simple and highly flexible, taking any kind of input, and supports numerous analytical tasks like weight and Explore the factors that contribute to a result and Classify e-mail, documents, or other objects that have many attributes.
Effectiveness of Data mining
Data mining technique is an effective modeling technique used in business to take effective decisions in the organizations. Data mining techniques gather the information from different areas and it also use the historical information. Before you can efficiently use data mining tools, you must have large amounts of information in storage. Data mining is a modeling process which transfer the information enfolded in a dataset into a form amenable to human cognition. Recently available tools of data mining are support only automatic modeling. Data mining tools are using in different areas effectively because of its features. These can be used to understand the business better and also exploited to improve future performance through predictive analytics. It is very useful for marketers because it provides perfect trend details and customers' purchasing behavior. In addition, data mining may also help marketers in predicting which products their customers may be interested in buying. Through this prediction, marketers can surprise their customers and make the customer's shopping experience becomes a pleasant one. Retail stores can also benefit from data mining in similar ways.Data mining can help effectively for financial institutions in areas such as loan information and credit reporting. For example, by examining previous customers with similar attributes, a bank can estimated the level of risk associated with each given loan. Additionally, data mining can also assist credit card issuers in detecting potentially fraudulent credit card transaction. Data mining can aid law enforcers in identifying criminal suspects as well as apprehending these criminals by examining trends in crime type, habit, location and other patterns of behaviors. Data mining can assist researchers by speeding up their data analyzing process. Thus, allowing those more time to work on other projects.Hence, from the above discussion it can be stated that data mining can be implemented in different areas like banking, crime and financial organizations. This technique is a powerful technique which is very useful technique to take the decisions.
Data mining is to get the knowledge from the large amount of data. Data mining is nothing but filtering the data available. In order to get the essential data the proper steps has to be taken and that can be used for any organization for their growth (Jiawei Han and Micheline Kamber, 2006).This is the science of obtaining the useful information from the large amount of data. The data from the data mining is characterized by participation of two or more fields of study (Hand .D.J, Heikkin Mannila and Padhraic Smyth, 2001). Data mining is cooperative effort of humans and the computers. The humans set the goals and the computers will set the appropriate data for the goals provided. Some of the applications of the data mining is money analysis is small business loans, IT organizations etc. Data mining has been one of the most animated areas in many organizations in the last few years. Data from the data mining can be used properly by data warehouse that integrates the various sectors of an organization, for example to get the data from the production process, contacts of suppliers, the sales and the contacts with the customers. This system provides the valuable information (Paolo Giudici and Silvia Figini, 2009). Hence from the above context it can be stated that data mining is an essential tool for any organization for faster growth by using the knowledge or useful data obtained by the data mining.
The main aim of this project is to implement the data mining in the e-health.
* To identify the problems facing by the health care organization
* To overcome problems and for maintaining proper records in health care system
* Importance of data mining in e-health
In this research it is discussed that data mining has successfully built up. From last few years data mining has developed many techniques and came with the many algorithms. Most of the organizations are having many hopes in the data mining which is expecting more benefits for the data ware house in the making of decision. The data mining mainly concentrates on the improvement of the technologies (Fosca giannoti and dino pedreschi, 2008). Various organizations are making use of data mining. Many scientific researches are showing interest towards data mining. Organizations like banking, business etc are using data mining (E. Vance Wilson, 2009). Data mining can be used in the business for improving the marketing and is used to identify the customer's requirements. In marketing cross selling the major problem by using the data mining it can be avoided. Data mining is also used in the banking sectors this will be very useful in identifying the credit card fraud users (Athina A. Lazakidou and konstatinos M. siaaaiakos, 2008). By using the data mining the information should be collected and using the information the risks obtained the fraud users can be identified. In this project it is mainly discussed about the uses of data mining in the E-health. Data mining will be used in the e-health to give support to the management. By using data mining it can be identified the people who stay in the rural areas where there is no availability of the medical services so, here by using the data mining the information can be collected by the data mining. Data mining can be used in the different areas of the e-health services. Patient information and the medical care that requires for the patient can be arranged properly by using the data mining.
Purpose of study
Data mining applications can be used in the various organizations. The data mining was used in the business purpose but now new technologies are introduced in the data mining which is very benefit to the organization. By making use of the new technologies many organizations are utilizing the data mining services. Data mining is used in the E-health. Using of the data mining in the health care is very useful (Llias g. Maglogiannis, Kostas karpouzis and manolis Wallace, 2006). For maintaining the records and for maintaining the patient records the data mining will play main role. Users can also separate out the data that is focused on their analysis on the list of records that is given in the data base. The records of the in and out patients can be maintained by using the e-health. Without making use of data mining it is difficult for the organizations to maintain the huge records.
Chapter 2 (3500)
2.1. Data Mining
Data Mining is the process which is utilized to analyze the data from the different views and summarizing it into practicable information and this information is used for increasing the revenue and for cutting the cost. For analyzing the data, data mining software is used and data mining is the one of analytical tools (Lan H. Witten and Eibe Frank, 2005). Data base technology has been characterized by the popular adoption of relational technology and developments activities on the new and database systems. Data mining allows client to allow data from various dimensions and will categorize it, and can summarize the relationships which is identified. Technically, data mining is the process of finding correlation between the many fields in large relational data. For solving business problems data mining software allows users to analyze large databases (Jiawei Han and Micheline Kamber, 2006). Data mining is just a technology it is not a business solution. Data mining can be performed on data represented in quantitative, textual, or multimedia forms. Data mining applications can use a variety of parameters to examine the data. They include association, sequence or path analysis, classification, clustering, and forecasting. Data mining tools predict future trends and behaviors allowing business to make active and to take decisions. The automated prospective analyses offered by data mining move beyond the analyses of past events provided by tools typical of decision support systems (Hand D.J., Heikki Mannila and Padhraic Smyth, 2001). Data mining tools can answer business questions that traditionally consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Most of the companies already collect and rectify huge quantities of data. Data mining techniques can be implemented quickly on existing software and hardware platforms to raise the value of existing information resources and can be integrated with new products and systems as they are brought online (Sushmita Mitra and Tinku, 2003). Various terms have been used to refer to data mining. These include data extraction. Some of the data mining techniques include those based on the rough sets, logic programming among others. Data mining process is not trivial it contains many steps such as business problem definition, data collection, data preprocessing etc. In every given step various types of techniques may applied. Due to the complexity of data mining process and data mining tools normal business users cannot easily use data mining tools to solve their business problems. Data mining practice in industry mostly depends on the experienced data mining professionals for providing the solutions. Data mining practice has become more costly and time consuming (Graham J. Williams and Simeon J. Simoff, 2006). It is integrated with many technologies such as visualization and parallel computing. It is being carried out in various fields. Database management researches are taking advantage of the work on the deductive and intelligent query processing for the data mining. These areas are interested to extend query processing techniques to facilitate data mining. Data warehousing is also another key data management technology for integrating the various data sources and organizing the data so that it can be effectively considered. Researches in statistical analysis are integrating their techniques with machine learning techniques for developing more techniques for data mining. Various analysis packages are now marketing by the data mining. It attracted a great deal of attention in the information industry from the past few years. The information and knowledge gained can be used for the applications ranging from the market analysis to production control and science exploration. Data mining can be viewed as a result of the natural evolution of information. The database system industry has witnessed an evolutionary path in the development of the functionalities. With the numerous database system offering query and transaction processing as common practice so that advance data analysis has naturally become the next target. Large scale information technology is developing separate transaction and analytical systems; data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on indeterminate user queries. Several types of analytical software are available where as statistical, machine learning, and neural networks. In statistical, stored data is used to locate data in predetermined groups (Jeffrey W. Seifert, 2004). Data items are grouped according to logical relationships or consumer preferences in clusters when considering the association, Data can be mined to identify associations and in sequential patterns Data is mined to anticipate behavior patterns and trends.
2.2 Significance of Data mining
Data mining helps to extract unsuspected data from very large databases. Data mining is an advanced tool for managing magnanimous data. The previously collected data will be analyzed which is the secondary analysis. Misuse detection searches the attack patterns which are known. The present generation of commercial intrusion detection systems has implemented this strategy. In data mining implicit, unknown and potentially useful information is extracted from database. The misuse detection systems include data mining in its strategy. JAM (Java Agents for Meta-learning) implements data mining techniques for discovering the intrusion patterns. Meta learning classifier is applied to analyze the signature of attacks. Features are extracted from corresponding algorithms which are used to compute models of intrusion behavior (Daniel Barbara and Sushil Jajodia, 2002). Therefore data mining in JAM builds a misuse detection model. Data warehousing has become affordable as the data mining techniques have reduced the costs involved in data processing. Data mining tools when implemented on high performance parallel processing systems can analyze the magnitude of databases in minutes. It helps the users to experiment with various models to understand the complex data. The speed factor makes it more practical to analyze the data. Data mining algorithms have existed for more than a decade but these algorithms are being used recently as mature, reliable. Understandable tools targeted to outperform older methods. Enhanced analytical models and algorithms like data visualization and exploration and others provide profound analytical depth.
By implementing data mining precisely businesses can mine data regarding customers' purchasing patterns, gain, behavior and a better understanding of the customer to help minimize the fraud, resource forecasting, to increase acquisition of the customer and finally to curb customer erosion. It helps in improving the production quality and to reduce the losses in production while manufacturing. Perfect implementation of data mining strategies helps in identifying the hidden patterns in a single step (George Fernandez, 2003). There are various data mining products the most prevalent are conditional rules or association rules. Conditional rules are drawn from induced trees while learning from tabular data is done in association rules. The most common among the two is association rules. Data mining offers several algorithms for various problems, at the same time learning data streams poses new challenges to data mining. In these situations training examples are generated at random. Natural approach for these kinds of incremental tasks consists of adaptive learning algorithms. The learning systems in data mining are able to exploit constant, high volume, open ended data streams. The properties of these systems are they require small constant time per data. It uses fixed amount of main memory irrespective of data. It has the potential to deal with changes in the target concept. To achieve these properties they require sampling and randomization techniques. Some data stream models allow delete and update operators (Pavel Brazdil, Christophe Giraud-Carrier, Carlos Soares and Ricardo Vilalta, 2008). Mining data stream aims to infuse knowledge structure represented in models and patterns. The crucial issue in data stream mining is to locate frequent patterns which are urged by business applications like e-commerce, recommender systems, supply chain management and group decision support systems. Many algorithms had been proposed constantly to make this fast and accurate (Reda Alhajj, 2007).
The signification of peer-to-peer downloading is examined, and the data mining technology is employed to P2P downloading detection. A model to detect P2P downloading is built. The Agriori algorithm is improved according to the given domain knowledge, and the detection performance of the algorithm improved is certified by experiments. Finally, the rules mined by the improved algorithm are interacted with firewall, and the utilization ratio of the campus network is promoted. Data mining consists of attempting to discover novel and useful knowledge from data, trying to find patterns among datasets that can help in intelligent decision making. Data mining in agriculture is a relatively novel research field. Efficient techniques can be developed and tailored for solving complex agricultural problems using data mining (Ian H. Witten and Eibe Frank, 2000) Recommendations for future research directions in agriculture-related fields can be provided. Due to the rapid growth of electronic data having graph structures such as HTML and XML texts and chemical compounds, many researchers have been interested in data mining and machine learning techniques for finding useful patterns from graph structured data (Sankar K. Pal, Pabitra Mitra and Pabitra Mitra, 2004 ). Since graph data contain a huge number of substructures and it tends to be computationally expensive to decide whether or not such data have given structural features, graph mining problems face computational difficulties. Data mining can frequently provide additional help than web search services. For instance authoritative web page analysis based on the linkage between the web pages can assist in the classification of web pages based on the importance, influence and topics. Web community analysis helps in identification of hidden Web Social networks and communities. Web mining is the development of scalable and efficient web data analysis and mining methods. It will help in distribution of information and to locate the web dynamics and the association and other relationships among various web pages.
Data Mining and Knowledge Data Discovery (Need to be done)
For drawing out the practical information from the immense repositories of the various types data, the recently emerged an important direction are Data mining and Knowledge data Discovery is used. These are used to know the different concept of the data mining tasks. In order to make progress in superiority of the health care of the patient without exploding the cost, the healthcare system offers the great potential in reducing the charge of the hospitalization (Joseph Tan and Joseph K. H. Tan, 2005). The process of Knowledge Data Discovery deals with the identification of the potential use and the features of patient motoring system. This is essential for discovering the relevant actionable patterns that contributes the modeling system (Yeal Song, Johann Eder and Tho Manh Nguyen, 2007). The has shown below give a complete detail of the KDD process of the e-health. This process work with the relevant data selection, feature extraction and construction is done with the help of the visual data. In this process the pattern discovery is used to give the full details in the form of the descriptive and predictive pattern of the patient modeling and the rules for adapting the constructing steps.
Chapter 3 (Need to be done)
E-Health is also known as electronic health. It supports electronic process and communication. E-Health is fully based through internet. All the e-health services are offered by information systems through internet so that anyone can share with anywhere at any time. Citizens are using internet and all e-health systems to find out the medicine and all health related information based on the requirement of their disease (Ton A.M. Spil and Roel Schuring, 2005). Initially this is used by industry and marketing people rather than academics. The transfer of this e-health knowledge to internet is inevitable. Health care is not more accessible; it should be revised and revolutionized. All the applications are updated with the newer applications. This makes people easy to understand the concepts of e-health. People can retrieve the updated information sitting at one place without consulting doctors or any other professionals. All these services are inexpensive and sometimes free since people access it through internet. It helps individuals to make their own decisions regarding their health (Marlene M. Maheu, Pamela Whitten and AceAllen, 2001). Hence from the above discussion it can be said that e-health is helpful for all the citizens mainly for the people who can't pay their medicinal bills and so on. Since the information is gathered from internet is free of cost.
E-Health system requirement gather
E-health is an end-to-end process from emergency to homecare. E-health from the information technology will support the Healthcare using new tools and software and hardware applications by fundamental redesign of healthcare systems. It should also maintain the electronic health records to maintain the easy communication between the patients and also healthcare professionals. The healthcare services by online will reduce the unnecessary examinations and also waiting queues which will improve the medical facilities to the patients. (George Demiris, 2004). In the e-health system healthcare-related components are like patient identification, health data communications and resources location. There should be collaboration between the patients and healthcare professionals, hospitals etc. some other generic components are licensing, security like authentication etc (Ilias lakovidis, Petra Wilson and Jean Claude Healy, 2004).
Analysis of E-health System Requirement
E-health is the use of information and communication technologies for health. E-health system generally should have the information regarding the patient. Generally, patient related medical information, non-medical information and many other things (Bernd Blobel, 2002). The e-health system should use all the available medical and healthcare facilities in order to for fast treatment. E-health addresses electronic communications for both clinical and non-clinical services (David McClure. P, 2007).
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