Data Mining

In: Business and Management

Submitted By guyvolo
Words 7432
Pages 30
Introduction: Technical Analysis & Data Mining

1 How Data Mining Is Related to Technical Analysis

Technical analysis (TA) is concerned with discovery of recurring patterns in financial market time series for the purpose of predicting and profiting from trends and trend reversals the prices of freely traded assets such as stocks, market indexes, exchange traded funds (ETF), commodities, currencies and financial futures and options .

Objective TA is restricted to patterns that can be represented numerically and trading systems that produce clear cut buy and sell signals that can be evaluated on historical data. Thus objective TA is concerned with the development of trading systems. Other forms of technical analysis rely upon the visual inspection and subjective interpretation of graphs to detect patterns and predict trends. Objective TA employs indicators, which are new time series derived by applying one or more mathematical transformations to raw market data such as price, volume, open-interest and other data series produced by trading activity. For example, technical analysts apply moving averages to identify price trends.

Data mining (DM) is also concerned with patterns and prediction and thus the natural fit between DM and objective TA. Data miners use specialized algorithms to analyze large data multivariate data bases containing thousands or even million of cases with the intent of discovering unobvious patterns that can be used to predict various kinds of outcomes. The end product of a DM effort is a predictive model based the discovered patterns. Ultimately the model is used to make predictions on future cases. By “future cases” we mean cases not present in the data base analyzed to produce the predictive model. Being able to “predict” used to create the model is easy, given a model of sufficient complexity. Being able to produce a model…...

Similar Documents

Data Mining

...Data Mining Jenna Walker Dr. Emmanuel Nyeanchi Information Systems Decision Making May 30, 2012 Abstract Businesses are utilizing techniques such as data mining to create a competitive advantage customer loyalty. Data mining allows business to analyze customer information, such as demographics and purchase history for a better understanding of what the customers need and what they will respond to. Data mining currently takes place in several industries, and will only become even more widespread as the benefits are endless. The purpose of this paper is to gain research and examine data mining, its benefits to businesses, and issues or concerns it will need to overcome. Real world case studies of how data mining is used will also be presented for a deeper understanding. This study will show that despite its disadvantages, data mining is an important step for a business to better understand its customers, and is the future of business marking and operational planning. Tools and Benefits of data mining Before examining the benefits of data mining, it is important to understand what data mining is exactly. Data mining is defined as “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 & Volonino, 2011). The information identified using data mining includes patterns indicating......

Words: 1900 - Pages: 8

Data Mining

...Data Mining 0. Abstract With the development of different fields, artificial intelligence, machine learning, statistic, database, pattern recognition and neurocomputing they merge to a newly technology, the data mining. The ultimate goal of data mining is to obtain knowledge from the large database. It helps to discover previously unknown patterns, most of the time it is followed by deeper manual evaluation to explain and correlate the results to establish a new knowledge. It is often practically used by government, bank, insurance company and medical researcher. A general basic idea of data mining would be introduced. In this article, they are divided into four types, predictive modeling, database segmentation, link analysis and deviation detection. A brief introduction will explain the variation among them. For the next part, current privacy, ethical as well as technical issue regarding data mining will be discussed. Besides, the future development trends, especially concept of the developing sport data mining is written. Last but not the least different views on data mining including the good side, the drawback and our views are integrated into the paragraph. 1. Introduction This century, is the age of digital world. We are no longer able to live without the computing technology. Due to information explosion, we are having difficulty to obtain knowledge from large amount of unorganized data. One of the solutions, Knowledge Discovery in Database (KDD) is......

Words: 1700 - Pages: 7

Data Mining

...DATA MINING FOR INTELIIGENCE LED POLICING The paper concentrates on use of data mining techniques in police domain using associative memory being the main technique. The author talks of constructing the data being easier and thus giving effective decision making. The author does mention making the process as simple as possible since there are not enough technically sound people into databases. The process involves a step procedural method. Further the author does explain the advantages of this system in police environment. The author mentions use of data mining activities by Dutch forces and how it makes the work easier to predict and analyze the scenario. The author talks about the tool and name given to it as Data detective. This tool involved a chunk of data stored in data warehouse. There has been a continuous development in the tool used here throughout the years making it more efficient than before. The data mining tool automatically predicts the trend and the lays down all the statistical report. This tool makes it easier for the police to pin out criminals and their trends easily. The process raises a challenge so that a predictive modeling can be developed better than before. The author talks about understanding the links and then predicting is important. The author also mentions that this involves pattern matching which is achieved by data mining. The tool also helps in automatic prediction of criminal nature matches a profile and this leads to be......

Words: 1306 - Pages: 6

Data Mining

...Assignment" in the Student Center. Instructors, training on how to grade is within the Instructor Center. Assignment 4: Data Mining Due Week 9 and worth 75 points The development of complex algorithms that can mine mounds of data that have been collected from people and digital devices have led to the adoption of data mining by most businesses as a means of understanding their customers better than before. Data mining takes place in retailing and sales, banking, education, manufacturing and production, health care, insurance, broadcasting, marketing, customer services, and a number of other areas. The analytical information gathered by data-mining applications has given some businesses a competitive advantage, an ability to make informed decisions, and better ways to predict the behavior of customers. Write a four to five (4-5) page paper in which you: Determine the benefits of data mining to the businesses when employing: Predictive analytics to understand the behavior of customers Associations discovery in products sold to customers Web mining to discover business intelligence from Web customers Clustering to find related customer information Assess the reliability of the data mining algorithms. Decide if they can be trusted and predict the errors they are likely to produce. Analyze privacy concerns raised by the collection of personal data for mining purposes. Choose and describe three (3) concerns raised by consumers. Decide if each of these......

Words: 493 - Pages: 2

Data Mining

...Data mining is an iterative process of selecting, exploring and modeling large amounts of data to identify meaningful, logical patterns and relationships among key variables.  Data mining is used to uncover trends, predict future events and assess the merits of various courses of action.             When employing, predictive analytics and data mining can make marketing more efficient. There are many techniques and methods, including business intelligence data collection. Predictive analytics is using business intelligence data for forecasting and modeling. It is a way to use predictive analysis data to predict future patterns. It is used widely in the insurance, medical and credit industries. Assessment of credit, and assignment of a credit score is probably the most widely known use of predictive analytics. Using events of the past, managers are able to estimate the likelihood of future events. Data mining aids predictive analysis by providing a record of the past that can be analyzed and used to predict which customers are most likely to renew, purchase, or purchase related products and services. Business intelligence data mining is important to your marketing campaigns. Proper data mining algorithms and predictive modeling can narrow your target audience and allow you to tailor your ads to each online customer as he or she navigates your site. Your marketing team will have the opportunity to develop multiple advertisements based on the past clicks of your visitors.......

Words: 1136 - Pages: 5

Data Mining

...Data Mining Objectives: Highlight the characteristics of Data mining Operations, Techniques and Tools. A Brief Overview Online Analytical Processing (OLAP): OLAP is the dynamic synthesis, analysis, and consolidation of large volumns of multi-dimensional data. Multi-dimensional OLAP support common analyst operations, such as: ▪ Considation – aggregate of data, e.g. roll-ups from branches to regions. ▪ Drill-down – showing details, just the reverse of considation. ▪ Slicing and dicing – pivoting. Looking at the data from different viewpoints. E.g. X, Y, Z axis as salesman, Nth quarter and products, or region, Nth quarter and products. A Brief Overview Data Mining: Construct an advanced architecture for storing information in a multi-dimension data warehouse is just the first step to evolve from traditional DBMS. To realize the value of a data warehouse, it is necessary to extract the knowledge hidden within the warehouse. Unlike OLAP, which reveal patterns that are known in advance, Data Mining uses the machine learning techniques to find hidden relationships within data. So Data Mining is to ▪ Analyse data, ▪ Use software techniques ▪ Finding hidden and unexpected patterns and relationships in sets of data. Examples of Data Mining Applications: ▪ Identifying potential credit card customer groups ▪ Identifying buying patterns of customers. ▪ Predicting trends of......

Words: 1258 - Pages: 6

Data Mining

...According to www.PredictiveAnalyticsWorld.com, “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 been trained over your data, learning from the experience of your organization. It continues to say, “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.” Predictive analytics are used to determine the probable future outcome of an event or the likelihood of a situation occurring. It is the branch of data mining concerned with the prediction of future probabilities and trends. Predictive analytics are used to automatically analyze large amounts of data with different variables; it includes clustering, decision trees, market basket analysis, regression modeling, etc. There are three main benefits of predictive analytics: minimizing risk, identifying fraud, and pursuing new sources of revenue. Being able to predict the risks involved with loan and credit origination, fraudulent insurance claims, and making predictions with regard to promotional offers and coupons are all examples of these benefits. This type of algorithm allows businesses to test all sorts of situations and scenarios it could take years to test in the real world. ......

Words: 1691 - Pages: 7

Data Mining

...Data Mining I found the topic of data mining very interesting in that it uncovers coveted information needed for improving and refining our daily lives. Information regarding traffic patterns, flight arrivals, consumer purchases, education, is collected and analyzed to improve a particular model. The data mining process is designed to gather information from a targeted sample which will enable companies to refine their business model in order to become more profitable. This process is not engineered to accumulate more information for an organization but to extract more meaningful information and correlate patterns of information that already exists in their data base. The importance of this information will allow companies to better analyze information to make quick effective decisions which will spur productivity. Data mining in turn can monitor and analyze these results to effectively manage assets. Organizations will be able to better predict the results of their decision making. How Data Mining Works A sample size is created by targeting large amounts of relevant information that is small enough to process. The information is then studied to find relationships which were anticipated , analyze trends, and recognize irregularities to gain knowledge for a design. “The data is then modified to transform the variables to focus the model selection process. A model is then selected by using analytical tools to search for a combination of data that......

Words: 888 - Pages: 4

Data Mining

...Data Mining Professor Clifton Howell CIS500-Information Systems Decision Making March 7, 2014 Benefits of data mining to the businesses One of the benefits to data mining is the ability to utilize information that you have stored to predict the possibilities of consumer’s actions and needs to make better business decisions. We implement a business intelligence that will produce a predictive score for those consumers to determine these possibilities. Predictive analytics is the 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. (Impact, 2014) The usefulness of predictive scoring is obvious. However, with no predictive model and no means to score your consumer, the possibility of gaining a competitive edge and revenue is also predictable. To discover consumer buying patterns from a transaction database, mining association rules are used to make better business decisions. However because users may only be interested in certain information from this database and do not want to invest a lot of time in searching for what they need, association discovery will assist in limiting the data to which only the end user needs. Association discovery will utilize algorithms to lessen the quantity of groupings of item sets or sequences in each......

Words: 1318 - Pages: 6

Data Mining

...Data Mining By Jamia Yant June 1st, 2012 Predictive Analytics and Customer Behavior “Predictive analysis is the decision science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right solution in the shortest time possible.” (Kaith, 2011) There are seven steps to Predictive Analytics: spot the business problem, explore various data sources, extract patterns from data, build a sample model using data and problem, Clarify data – find valuable factors – generate new variables, construct a predictive model using sampling and validate and deploy the model. By using this method, businesses can make fast decisions using vast amounts of data. There are three main benefits of predictive analytics: minimizing risk, indentifying fraud, and pursuing new sources of revenue. Being able to predict the risks involved with loan and credit origination, fraudulent insurance claims, and making predictions with regard to promotional offers and coupons are all examples of these benefits. It basically reduces the cost of making mistakes. This type of algorithm allows businesses to test all sorts of situations and scenarios it could take years to test in the real world. Studying customer behavior gives businesses a competitive advantage and allows them to stay ahead of the competition in their market place. Associations Discovery and Customer Purchases Association analysis is useful for discovering interesting......

Words: 1650 - Pages: 7

Data Mining

...DATA MINING Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of tools for analyzing data. It allows users to analyze data from many different dimensions or angels, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding patterns among dozens of fields in large databases that are similar to one another. Data is any facts, numbers, or text that can be processed by a computer so in general it makes it easier for a company or business to see what the majority of customers want at a time. It’s almost like a survey that we don’t realize we are taking. I think it really can benefit consumers because we can walk into a place of business and see what we want on the shelves because it is in demand. Even better, the things we don’t want to purchase are not there because there is no demand for it. It gives us the choice to be heard and have a say in making decisions on things that impact us most. Information can be converted into knowledge about historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in light of promotional efforts to provide knowledge of consumer buying behavior. Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts. I don’t think......

Words: 1315 - Pages: 6

Data Mining

...ITT TECHNICAL INSTITUTE DATA MINING REPORT ONE AND TWO Shirly Mu, Todd Hughson, James Wall PROBLEM SOLVING THEORY ONSITE COURSE GS1140 INSTUCTOR LJILJANA MORRIS Today we are doing a project report on Costco. For Sol and Robert Price in 1976 they asked friends and family to help out with an opening price of two point five millon to open Price Club on July twelfth, they open their shop in an air hanger on Boulevard in San Diego, California. They were originally going to serve only small business. Mr. Price found out that it will be more beneficial to serve select customers. Costco was founded by James Sinegal and Jeffery H. Brotman. Costco opened its doors in 1983 in Seattle, Washington. Price Club and Costco later merged and renamed the business PriceCostco. And in 1997 due to its success the name was changed again to Costco Wholesale. (About History, 2014) Costco Wholesale stores headquarters are located in Issaquah, WA. The mission statement for Costco is to continually provide our members with quality goods and services at the lowest possible prices. (Farfan, 2014) There are three types of membership cards at Costco; Executive membership, Business membership and Gold membership. The one I have is executive membership this cost about one hundred and twenty dollars sounds like a lot but I’ll be able to get two percent back in my shopping. If I do not get back more than fifty five dollars back for the entire year they will give me that amount or the two percent......

Words: 1306 - Pages: 6

Data Mining

...(YourFirstName, YourLastName) University Name Data Modeling While the importance of data mining huge volumes of data from expansive volumes of data cannot be gainsaid, there are several shortcomings of data mining as outlined in the US Government’s General Accounting Data Mining Report. The unearthed findings are discussed below. Nascent Data Mining Efforts It is reported that out of the 128 federal departments and agencies surveyed on their use of data mining, it can be revealed that only 52 agencies are using or are planning to use data mining. This means that more than half of the government’s departments are yet to harness the power of data mining. The implementation of data mining poses a variety of challenges such as due to human factors like the learning curve effect, spelling and referencing mistakes as well as systematic factors such as incoherent logic and system failures. The federal government outsources the initial implementation process to third party specialists and this poses the threat of sensitive information finding its way to malicious nosey individuals like the Edward Snowdens of this world. As such, these privacy concerns are valid. Classification Of paramount importance is the arranging and grouping of data into meaningful classes if the information to be generated from the data mining is to be of any sense. For example, there is the likelihood that Grantee Monitoring Activities Offices in the Department of Agriculture do not......

Words: 1256 - Pages: 6

Data Mining

...Lawrence Bandy Leslin Cruz Wen. P.M. Feb. 15 2016 General Data Mining (Part1) * What is data mining and how can it benefit/ not benefit society? Data mining is a technique that is used to analyze and collect data from different area of everyone life. Also Data mining gathers mathematics, genetics and marketing to analyze data from different dimensions or angles to put in an organize graph or data sheet for research proposes. It can benefit society by organize a data sheet for mangers or bosses of a company that needs to purchase products to see what is the most selling item that needs stocked. It also could not benefit society if the personal in charge will not take the time and effort to put the right information into the data base. * Will it ultimately lead to behavior control? Why and How? The behavior control will not ultimately effect the individual. The reason is that the person is entitled to their own decisions on purchasing what they want. How, no one can decide for you. You control your own money and spend it the way you want to. * What is the Clustering Analysis? Is a way of grouping a set of objects in a way that objects in the same group are very similar to each other than those in other groups. * What is Anomaly or Outlier Detection? Is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. * What is the Association Rule? It is a method to intended to......

Words: 711 - Pages: 3

Data Mining

...The increasing use of data mining by corporations and advertisers obviously creates apprehension from the perspective of the individual consumer due to privacy, security and the potential use of inaccurate information. The idea that there are data warehouses that contain customers’ personal information can be rather frightening. However, the use of data mining by these organizations can also lead to numerous benefits for consumers they otherwise would not have realized. Besides the obvious benefit of guiding consumers to products or services they’d be more interested in purchasing, the use of data mining by companies has also benefitted individuals’ health and financial safety. Not long after the use of data mining came into prominence the use of data mining consumer information vs. consumer privacy became a major issue in early 1998 after CVS and Giant entered into an agreement with Elensys, a Massachusetts direct marketing company, to send reminders to customers who had not renewed their prescriptions. However, neither CVS nor Giant explained how the program would work or received their customers' permission to transfer their prescription records to a third party (Pyatt Jr.). Giant and CVS’s rationale for entering into this agreement was “to develop a positive program to benefit consumers, many of whom don't take their medication properly,” (Pyatt Jr.). Even though their primary intention was good, Giant and CVS were not transparent about their agreement with......

Words: 949 - Pages: 4