...spectrometric data analysis By XIAO Jiali, Jenny ( 0830300038) A Final Year Project thesis (STAT 4121; 3 Credits) submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Statistics at BNU-HKBU UNITED INTERNATIONAL COLLEGE December, 2011 DECLARATION I hereby declare that all the work done in this Project is of my independent effort. I also certify that I have never submitted the idea and product of this Project for academic or employment credits. XIAO Jiali, Jenny (0830300038) Date: ii Application of Bootstrap method in spectrometric data analysis XIAO Jiali, Jenny Science and Technology Division Abstract In this project the bootstrap methodology for spectrometric data is considered. The bootstrap can also compare two populations, without the normality condition and without the restriction to comparison of means. The most important new idea is that bootstrap resampling must mimic the separate samples design that produced the original data. Bootstrap in mean, bootstrap in median, and bootstrap in confidence interval are three kinds of effective way to handle mass spectrometric data. Then,we need to reduce dimension based on bootstrap method. It may allow the data to be more easily visualized. Afterwards, using results obtained by bootstrap, we use data mining method to predict a patient has ovarian cancer or not. Decision tree induction and neural network are usual way to classify it. Keywords: Bootstrap, data mining...
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...CIS 500 Complete ClasCIS 500 Complete Class Assignments and Term Paper Click link Below To Download Entire Class: http://strtutorials.com/CIS-500-Complete-Class-Assignments-and-Term-Paper-CIS5006.htm CIS 500 Complete Class Assignments and Term Paper CIS 500 Assignment 1 Predictive Policing CIS 500 Assignment 2: 4G Wireless Networks CIS 500 Assignment 3 Mobile Computing and Social Networking CIS 500 Assignment 4 Data Mining CIS 500 Term Paper Mobile Computing and Social Networks CIS 500 Assignment 1 Predictive Policing Click link Below To Download: http://strtutorials.com/CIS-500-Assignment-1-Predictive-Policing-CIS5001.htm In 1994, the New York City Police Department adopted a law enforcement crime fighting strategy known as COMPSTAT (COMPuter STATistics). COMPSTAT uses Geographic Information Systems (GIS) to map the locations of where crimes occur, identify “hotspots”, and map problem areas. COMPSTAT has amassed a wealth of historical crime data. Mathematicians have designed and developed algorithms that run against the historical data to predict future crimes for police departments. This is known as predictive policing. Predictive policing has led to a drop in burglaries, automobile thefts, and other crimes in some cities. Write a four to five (45) page paper in which you Compare and contrast the application of information technology (IT) to optimize police departments’ performance to reduce crime versus random patrols of the streets...
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...Data Mining 6/3/12 CIS 500 Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information. This information can be used to increase revenue, cut costs or both. Data mining software is a major analytical tool used for analyzing data. It allows the user to analyze data from many different angles, categorize the data and summarizing the relationships. In a nut shell data mining is used mostly for the process of finding correlations or patterns among fields in very large databases. What ultimately can data mining do for a company? A lot. Data mining is primarily used by companies with strong customer focus in retail or financial. It allows companies to determine relationships among factors such as price, product placement, and staff skill set. There are external factors that data mining can use as well such as location, economic indicators, and competition of other companies. With the use of data mining a retailer can look at point of sale records of a customer purchases to send promotions to certain areas based on purchases made. An example of this is Blockbuster looking at movie rentals to send customers updates regarding new movies depending on their previous rent list. Another example would be American express suggesting products to card holders depending on monthly purchases histories. Data Mining consists of 5 major elements: • Extract, transform, and load transaction data onto the data...
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...by other articles in PMC. Go to: Abstract Knowledge about users and their information needs can contribute to better user interface design and organization of information in clinical information systems. This can lead to quicker access to desired information, which may facilitate the decision-making process. Qualitative methods such as interviews, observations and surveys have been commonly used to gain an understanding of clinician information needs. We introduce clinical information system (CIS) log analysis as a method for identifying patient-specific information needs and CIS log mining as an automated technique for discovering such needs in CIS log files. We have applied this method to WebCIS (Web-based Clinical Information System) log files to discover patterns of usage. The results can be used to guide design and development of relevant clinical information systems. This paper discusses the motivation behind the development of this method, describes CIS log analysis and mining, presents preliminary results and summarizes how the results can be applied. Go to: INTRODUCTION The availability of clinical information to the clinician at the point of care is essential to the health care process. Inability to locate needed information can be costly in terms of time and quality of care. Clinical information systems have been developed to assist clinicians with their decisions; however, these systems need to ensure that they provide the information in optimal ways. In order...
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...Assignment 4: Data Mining CIS 500 Dr. Besharatian Submitted by: Eric Spurbeck December 7, 2013 Abstract This paper will discuss the process of data mining, how it is used, for what purpose it is used and what information can be gathered from the data, which is compiled from data mining. Assignment 4: Data Mining Webopedia (2013) defines data mining as, "A class of database applications that look for hidden patterns in a group of data that can be used to predict future behavior. For example, data mining software can help retail companies find customers with common interests." This means that large groups of data that is derived by information obtained through customers, customer purchases and customer buying habits. Businesses use this information for a variety of reasons; it is used for purchasing merchandise, tracking how certain merchandise is selling and even customers buying habits. Webopedia goes on to state that "data mining is popular in the science and mathematical fields but also is utilized increasingly by marketers trying to distill useful consumer data from Web sites." Predictive analytics are used to understand customer's behaviors, according to the article Predictive Analytics with Data Mining: How It Works (Siegel, Feb. 2005) it describes how this method has a predictor. This is "a single value measured for each customer" this is based on the customers purchased over a period and sets higher values for the most recent customer purchases. The...
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...Data Mining/Data Warehousing Matthew P Bartman Strayer University Ibrahim Elhag CIS 111– Intro to Relational Database Management June 9, 2013 Data Mining/Data Warehousing When it comes to technology especially in terms of storing data there are two ways that it can be done and that is through data mining and data warehousing. With each type of storage there are trends and benefits. In terms of data warehousing there are 5 key benefits one of them being that it enhance business intelligence. What this means is that business processes can be applied directly instead of things having to be done with limited information or on gut instinct. Another benefit of data warehousing is that it can also save time meaning that if a decision has to be made the data can be retrieved quickly instead of having to find data from multiple sources. Not only does data warehousing enhance business intelligence and save time but it can also enchance data quality and consistency.This is accomplished by converting all data into one common format and will make it consistent with all departments which ensures accuracy with the data as well. While these key benefits another one is that it can provide historical intelligence which means that analayze different time periods and trends to make future predictions. One other key benefit is that it provides a great return on investment. The reason being that a data warehouse generates more revenue...
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...FINAL REPORT DATA MINING Reported by: Nguyen Bao An – M9839920 Date: 99/06/16 Outline In this report I present my study in the Data mining course. It includes my two proposed approaches in the field of clustering, my learn lessons in class and my comment on this class. The report’s outline is as following: Part I: Proposed approaches 1. Introduction and backgrounds 2. Related works and motivation 3. Proposed approaches 4. Evaluation method 5. Conclusion Part II: Lessons learned 1. Data preprocessing 2. Frequent pattern and association rule 3. Classification and prediction 4. Clustering Part III: My own comments on this class. I. Proposed approach • An incremental subspace-based K-means clustering method for high dimensional data • Subspace based document clustering and its application in data preprocessing in Web mining 1. Introduction and background High dimensional data clustering has many applications in real world, especially in bioinformatics. Many well-known clustering algorithms often use a whole-space distance score to measure the similarity or distance between two objects, such as Euclidean distance, Cosine function... However, in fact, when the dimensionality of space or the number of objects is large, such whole-space-based pairwise similarity scores are no longer meaningful, due to the distance of each pair of object nearly the same [5]. ...
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...Data Mining Prepared by: Kirsten Sullivan Strayer University CIS 500 Dr. Baab September 9, 2012 Data mining is a concept that companies use to gain new customers or clients in an effort to make their business and profits grow. The ability to use data mining can result in the accrual of new customers by taking the new information and advertising to customers who are either not currently utilizing the business's product or also in winning additional customers that may be purchasing from the competitor. Generally, data are any “facts, numbers, or text that can be processed by a computer.”1 Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes operational or transactional data such as, sales, cost, inventory, payroll, and accounting. Data mining also known as “knowledge discovery”, is the process of analyzing data from different perspectives and summarizing it into useful information- information that can then be used to increase revenue, cuts costs, and continue the goals outlined for the company. Data mining consists of five major elements: “Extract, transform, and load transaction data onto the data warehouse system, store and manage the data in a multidimensional database system, provide data access to business analysts and information technology professionals, analyze the data by application software, present the data in a useful format, such as a graph or table.”2...
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...Assignment 4: Data Mining CIS 500 Professor: Dr. Edwin Otto Strayer University August 30, 2013 “Data mining is a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases, including data warehouses” (Turban, 2011). Predictive analytics serves as a benefit of data mining because it’s a process that uses machine learning to analyze data and make predictions. This can be beneficial to a business because it can be helpful in understanding the behavior of customers. A good example of this would be a business using predictive analytics to decide what level of pricing should be used in correlation with sales information. A business could look at historical data for products, sales, and customers to determine the price for a given product and customer at the right time. Amazon is a heavy user of predictive pricing (Mehra, 2013). This technique is also used in Supply Chain Management because it helps you to understand consumer demand to manage the overall process. This includes delivery, returns, forecasting, sourcing, planning, and order fulfillment. The advantage is if a retailer can predict revenue from a specific product in a reasonable amount of time it will result in better inventory management, use of space, cash flow, and the elimination of out of stock items. Association discovery in products sold to customers is used to...
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...Data Mining Sherri White Dr. Edwin Otto CIS 500 Information System Decision Making September 2, 2012 Determine the benefits of data mining to the businesses when employing: 1. Predictive analytics to understand the behavior of customers Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization. Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer's predictive score informs actions to be taken with that customer. 1. Associations discovery in products sold to customers The way in which companies interact with their customers has changed dramatically over the past few years. A customer's continuing business is no longer guaranteed. As a result, companies have found that they need to understand their customers better, and to quickly respond to their wants and needs. In addition, the time frame in which these responses need to be made has been shrinking. It is no longer possible to wait until the signs of customer dissatisfaction are obvious before action must be taken. To succeed, companies must be proactive and anticipate what a customer desires. For an example in the old days, the store keepers would simply...
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...e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / d s s Detection of financial statement fraud and feature selection using data mining techniques P. Ravisankar a, V. Ravi a,⁎, G. Raghava Rao a, I. Bose b a b Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad 500 057, AP, India School of Business, The University of Hong Kong, Pokfulam Road, Hong Kong a r t i c l e i n f o a b s t r a c t Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer Feed Forward Neural Network (MLFF), Support Vector Machines (SVM), Genetic Programming (GP), Group Method of Data Handling (GMDH), Logistic Regression (LR), and Probabilistic Neural Network (PNN) to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 Chinese companies and compared with and without feature selection. PNN outperformed all the techniques without feature selection, and GP and PNN outperformed others with feature selection and with marginally equal accuracies. © 2010 Elsevier B.V. All rights reserved. Article history: Received 20 November 2009 Received in revised form 14 June 2010 Accepted 3 November 2010 Available online 12 November 2010 Keywords: Data mining Financial fraud detection Feature selection t-statistic Neural networks SVM GP 1. Introduction Financial fraud is a serious problem...
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...particularly useful in many applications. In this paper, we aim to advance a worldwide classification model based on the Naïve Bayes classification scheme. The Naïve Bayes classification is chosen because of its applicability in case of its previous history. For Confidentiality-preservation of the data, the concept of counterweight providing reliable party is used. Keywords- Confidentiality-preservation, Naïve Bayes, distributed databases, partition. I. INTRODUCTION In current years, There has been an advancement of computing and...
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...Data Mining By: Holly Gildea CIS 500 Dr. Janet Durgin June 09, 2013 Data Mining We learn that data mining is a method of evaluating data from different viewpoints and summarizing it into useful information. Such information can be beneficial and used to increase things like revenue, and cutting costs, and so on. There are four categories that we will look at and determine the benefits for in regards to data mining: predictive analytics to understand the behavior of customers, associations discovery in products sold to customers, web mining to discover business intelligence from web customers, and clustering to find related customer information. To understand the behavior of customers by the use predictive analytics we must first understand what predictive analytics is. “Predictive analytics is the process of dealing with a variety of data and applying various mathematical formulas to discover the best decision for a given situation” (ArticleSnatch, 2011). This gives any business a competitive edge and helps to remove the guess work out of the decision making process therefore helping to find the right solution in a shorter amount of time. In order to find the solution faster there are a seven simple steps that must be worked thru first: what is the problem for the company, searching for multiple data resources, take the patterns that are observed from that data, creating a model that contains the problem and the data, categorize the data and find important...
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...ASSIGNMENT: BIG DATA CHALLENGES CASE STUDY TITLE: CONVERTING DATA INTO BUSINESS VALUE AT VOLVO STUDENT’s NAME: JOSEPH OSASUMWEN LECTURER’s NAME: PROF. HOSSEIN FIROUZI COURSE TITLE: CIS 500 DATE: JANUARY 28, 2013. ABSTRACT Big data has posed both challenges and opportunity in our present world of technology sciences. The challenges related to searching, analyzing, manipulating, and organizing are experienced when data explode, this challenges cannot be assign to one sector or field because it has encroach almost all sector relating to healthcare, retailing, manufacturing, governmental institutions, financial services, physical and life sciences. These big data when well managed with the right strategies and IT infrastructure, can support organization in analysis, decision making, better business intelligence, enhance communications capabilities and enrich collaboration, like in the case of VOLVO car corporation whose challenges was transform to better anticipated needs of customer, ad-hoc analysis and employees making inform decision other than guess work. HOW VOLVO CAR CORPORATION INTEGRATED THE CLOUD INTO ITS NETWORKS. Volvo need a global IT infrastructure because its customers are globally, which inspired the car company to select WINDOWS AZURE. The company wanted a new infrastructure to match their huge data challenges, the cloud infrastructure uses the ETL (extract transform load) to stream huge data from the previous DBMS (database management systems)...
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...DDS, BI, Business Analytics, and Predictive Analytics LaShonda Spell Prof. S. Mirajkar CIS 356 Operating a successful business today involves utilizing the correct tools to make the best decisions for that business. The main tools that are used for making critical business decisions are DSS, DDS, BI, Business Analytics and Predictive Analytics systems. The concepts/ systems mentioned assist management in the major decision-making processes by providing crucial operational data in comprehensible formats for monitoring/ reviewing and analyzing. Making the best decisions regarding business operations determine the success or failure of the company and ensures that all business strategies are implemented and effective. In this essay, there is a brief overview of the similarities/ differences, methodologies/ technologies and evaluation of the capabilities of DDS, BI, Business Analytics and Predictive Analytics systems. Similarities and Differences among DDS, BI, Business Analytics, and Predictive Analytics regarding business scope/origins /histories/ methodologies/ technologies DDS (Data Distribution Service) are data communications based on the standards managed by the OMG (Object Management Group). The standards set by the OMG of DDS describe different latency levels of data communications for distributed applications (Twin Oaks Computing, Inc., 2011). DDS standard support data defining applications, dynamic publishing/ subscribing discovery and QoS policy configuration...
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