Customer center

We are a boutique essay service, not a mass production custom writing factory. Let us create a perfect paper for you today!

Example research essay topic: Saddle River Nj Prentice Hall Upper Saddle River Nj Prentice - 1,718 words

NOTE: Free essay sample provided on this page should be used for references or sample purposes only. The sample essay is available to anyone, so any direct quoting without mentioning the source will be considered plagiarism by schools, colleges and universities that use plagiarism detection software. To get a completely brand-new, plagiarism-free essay, please use our essay writing service.
One click instant price quote

Term Paper Data Mining in Financial Decision Making Table of contents Abstract... 3 Introduction... 4 Data mining... 6 Functions... 6 Classification 6 Associations... 7 Clustering/Segmentation... 7 Cluster Analysis 8 Conclusion... 10 Bibliography... 11 Abstract With many companies competitive pressure increasing, new technologies and computing power ability improving, they want to use some technology to increase revenues. Among many technologies, recently, they trend to use data mining to dig out customers buying patterns from mountains of sales data. In generally, the data mining process can be divided four stages: Identifying the problem, Analyzing the data, tack action, measuring the outcome. Among these stages, the first and the third belong to business issues. The paper will first define and describe what is data mining.

It will also seek to determine why and how data mining is useful in financial decisions. It will also show that data mining is concerned with the analysis of data and the use of techniques for finding patterns and regularities in sets of data. The background of data mining will be investigated in order to validate the claims of data mining in decision making. Introduction Let me first describe what data mining is. There has been a dramatic increase in the amount of information or data being stored in electronic format. The increase in use of electronic data gathering devices such as point-of-sale, web pages, or remote sensing devices has contributed to this explosion of available data.

Data storage became easier as the availability of large amounts of computing power at low cost i. e. the cost of processing power and storage is falling, made data cheap. There was also the introduction of new machine learning methods for knowledge representation based on logic programming in addition to traditional statistical analysis of data. The new methods tend to be computationally intensive hence a demand for more processing power. It is recognized that information is at the heart of business operations and various financial decisions.

Decision-makers as well as people from financial departments could make use of the data stored to gain valuable insight into the business. Database Management systems gives access to the data stored but this is only a small part of what can be gained from the data. Traditional on-line transaction processing systems, OLTP, are good at putting data into databases quickly, safely and efficiently but are not good at delivering meaningful analysis in return. This system is very useful in financial decisions thought it is not enough to make correct analysis and reports. Basically, data mining is concerned with the analysis of data and the use of software techniques for finding patterns and regularities in sets of data. It is the computer that is responsible for finding the patterns by identifying the underlying rules and features in the data.

Data mining is asking a process engine to show answers to questions we do not know how to ask (Bischoff & Alexander, June 1997, p 310). Data mining analysis tends to work from the data up and the best techniques are those developed with an orientation towards large volumes of data, making use of as much of the collected data as possible to arrive at reliable conclusions and decisions. And financial decision is that sphere where reliable, exact analysis is needed. Data mining Data Mining Functions Now let me proceed to the body part that explains in detail data mining functions and how they are applied to the financial decision making. Data mining methods may be classified by the function they perform or according to the class of application they can be used in. Some of the main techniques used in data mining are described below.

Classification Learning to map an example into one of several classes (Lain, July 1999, p 254) as the book describes classification. Data mining tools have to infer a model from the database, and in the case of supervised learning this requires the user to define one or more classes. The database contains one or more attributes that denote the class of a tuple and these are known as predicted attributes whereas the remaining attributes are called predicting attributes. A combination of values for the predicted attributes defines a class.

When learning classification rules the system has to find the rules that predict the class from the predicting attributes. Firstly the user has to define conditions for each class, the data mining system then constructs descriptions for the classes. Basically the system should, given a case or tuple with certain known attribute values, be able to predict what class this case belongs to. Once classes are defined the system should infer rules that govern the classification therefore the system should be able to find the description of each class.

The descriptions should only refer to the predicting attributes of the training set so that the positive examples should satisfy the description and none of the negative. A rule said to be correct if its description covers all the positive examples and none of the negative examples of a class. A rule is generally presented as, if the left hand side (LHS) then the right hand side (RHS), so that in all instances where LHS is true then RHS is also true, are very probable. The categories of rules are: exact rule - permits no exceptions so each object of LHS must be an element of RHS strong rule - allows some exceptions, but the exceptions have a given limit probabilistic rule - relates the conditional probability P (RHS|LHS) to the probability P (RHS) Other types of rules are classification rules where LHS is a sufficient condition to classify objects as belonging to the concept referred to in the RHS.

Associations Given a collection of items and a set of records, each of which contain some number of items from the given collection, an association function is an operation against this set of records which return affinities or patterns that exist among the collection of items. These patterns can be expressed by rules such as " 56 % of all the records that contain items A, B and C also contain items D and E. " The specific percentage of occurrences (in this case 56) is called the confidence factor of the rule. Also, in this rule, A, B and C are said to be on an opposite side of the rule to D and E. Associations can involve any number of items on either side of the rule. Clustering/Segmentation Clustering and segmentation are the processes of creating a partition so that all the members of each set of the partition are similar according to some metric. A cluster is a set of objects grouped together because of their similarity or proximity.

Objects are often decomposed into an exhaustive and / or mutually exclusive set of clusters. Clustering according to similarity is a very powerful technique, the key to it being to translate some intuitive measure of similarity into a quantitative measure. When learning is unsupervised then the system has to discover its own classes i. e.

the system clusters the data in the database. The system has to discover subsets of related objects in the training set and then it has to find descriptions that describe each of these subsets. There are a number of approaches for forming clusters. One approach is to form rules, which dictate membership in the same group based on the level of similarity between members.

Another approach is to build set functions that measure some property of partitions as functions of some parameter of the partition. Cluster Analysis In an unsupervised learning environment the system has to discover its own classes and one way in which it does this is to cluster the data in the database. Clustering and segmentation basically partition the database so that each partition or group is similar according to some criteria or metric. Clustering according to similarity is a concept, which appears in many disciplines.

If a measure of similarity is available there are a number of techniques for forming clusters. Membership of groups can be based on the level of similarity between members and from this the rules of membership can be defined. Another approach is to build set functions that measure some property of partitions i. e. groups or subsets as functions of some parameter of the partition. This latter approach achieves what is known as optimal partitioning.

Many data mining applications make use of clustering according to similarity for example to segment a client / customer base. Clustering according to optimization of set functions is used in data analysis. Clustering / segmentation in databases are the processes of separating a data set into components that reflect a consistent pattern of behavior. Once the patterns have been established they can then be used to divide data into more understandable subsets and also they provide sub-groups of a population for further analysis or action, which is important when dealing with very large databases.

Conclusion Data mining is the area of knowledge discovery that takes the transformed data and finds the patterns. The pattern found is transformed into knowledge that is used by users to make calculated decisions. This paper defined data mining as asking for answers to questions we do not know how to ask that were basically related to the financial decisions. This is done by using the data mining functions of classification, associations, sequential / temporal patterns, and clustering / segmentation . The techniques used by data mining are the cluster analysis, induction, decision trees, rule induction, and neural networks. Financial decision making greatly depends on the data mining and the choice of its appropriate technique.

Bibliography Berry, M. J. A. (1997). Data mining techniques: for marketing, sales, and customer. New York: Wiley. Bischoff, J. (1997).

Joyce Bischoff & Ted Alexander, (Eds. ). Data warehouse: Practical advise from the experts. Upper Saddle River, NJ: Prentice Hall. Lain, J. P. (1999).

Data mining neural networks: solving business problems from application development to decision support. NY: McGraw Hill. Camera, P. (1996). Discovering data mining: from concept to implementation. Upper Saddle River, NJ: Prentice Hall. Fayyad, U.

M. (1996). Advances in knowledge discovery and data mining. Menlo Park, CA: AAAI Press: MIT Press. Jack, C. (1998). Data mining: A hands on approach for business professionals.

Saddle River, NJ: Prentice Hall.


Free research essays on topics related to: saddle river nj prentice hall, upper saddle river nj prentice, data mining, neural networks, decision making

Research essay sample on Saddle River Nj Prentice Hall Upper Saddle River Nj Prentice

Writing service prices per page

  • $18.85 - in 14 days
  • $19.95 - in 3 days
  • $23.95 - within 48 hours
  • $26.95 - within 24 hours
  • $29.95 - within 12 hours
  • $34.95 - within 6 hours
  • $39.95 - within 3 hours
  • Calculate total price

Our guarantee

  • 100% money back guarantee
  • plagiarism-free authentic works
  • completely confidential service
  • timely revisions until completely satisfied
  • 24/7 customer support
  • payments protected by PayPal

Secure payment

With EssayChief you get

  • Strict plagiarism detection regulations
  • 300+ words per page
  • Times New Roman font 12 pts, double-spaced
  • FREE abstract, outline, bibliography
  • Money back guarantee for missed deadline
  • Round-the-clock customer support
  • Complete anonymity of all our clients
  • Custom essays
  • Writing service

EssayChief can handle your

  • essays, term papers
  • book and movie reports
  • Power Point presentations
  • annotated bibliographies
  • theses, dissertations
  • exam preparations
  • editing and proofreading of your texts
  • academic ghostwriting of any kind

Free essay samples

Browse essays by topic:

Stay with EssayChief! We offer 10% discount to all our return customers. Once you place your order you will receive an email with the password. You can use this password for unlimited period and you can share it with your friends!

Academic ghostwriting

About us

© 2002-2024 EssayChief.com