Literature Review On Seven Data Mining Techniques Essay

One of the biggest advantages of Neural Network is that it can actually learn from observing data sets. This way it uses a random function approximation tool, which helps to estimate the most efficient and ideal solution while defining all the computing functions and distributions. Neural Networks takes data samples instead of entire data sets to arrive to a solution, which saves a lot of time and money. Neural Networks are considered as simple mathematical models to enhance existing data analysis technology.

Instead we use the original predictors to predict the response. The original dataset was split into a training set that consists of 75% of the total observations and a test set that consists of 25% of the total observations. Observations were chosen randomly. Supervised learning methods was conducted on the training set to obtain a model, then the model was used on the test set to assess the prediction performance. The values for “K” in KNN were tuned via cross-validation. Due to the volume of the data, the “cost” parameter in the SVM was chosen somewhat ad hoc and the “mtry” parameter in the random forest was chosen as default. The error rates are as

Over the decades there were tremendous amount of challenges for every business. Customers have more knowledge, they have more options, and they have higher expectations. Customers are more informed with the humungous development in technology. Having more options in front of them, expectations has surpassed in retail industry. Loyalty is a customer having faith that your organization’s product or services offered is the best for them. It is the process of tapping the buying pattern of customers in a store based on their preferences. Customer loyalty is significant because it is economical to retain the old customers rather than acquiring new customers. So, organizations employ loyalty programs which reward customers for their repeat business.

I worked on a dataset that analyzed the malignant and benign prostate tissues that provided insight into a gene expression signature for prostate cancer. The goal was to conduct several prediction algorithms including principal component regression, elastic nets, partial least squares on the samples containing gene expression profiling by array to accurately predict prostate cancer. It was based on variables like factors that included disease state, and genotype/phenotype

Logistic regression is used in this study to compare the non-linear neural network approach to come up with the best influenza vaccination model to be used for prediction purpose in the medical practice of primary health care physicians; where the vaccine is normally dispensed. In this study, logistic regression has been used widely to analyze

Logistic regression model, as a usual approach before, was used to analyze the stroke outcomes' data. Fortunately, because of its potentially more powerful high-level prediction performance, machine learning algorithms have been proposed as an alternative to analyze large-scale multivariate data. Support vector machine (SVM) is one of the most popular machine learning methods to use for recognition or classification. Support vector machine (SVM) is one of the most popular machine learning methods used for recognition or classification.

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

There has been a major increase in the amount of health-related data since the introduction of the electronic medical record (EMR), and predictive modeling is one of the most interesting uses of it all. Predictive modeling is a simple concept. In doing this, you are taking all of your available data and creating numerous scenarios, or models, of how things could turn out. You are essentially using data to make predictions on the health of individuals and of the general public.

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Partial least squares (PLS) regression is a recent technique that combines features from and generalizes principal component analysis (PCA) and multiple linear regression. Its goal is to predict a set of

Question 1: Assume a base cuboid of 10 dimensions contains only three base cells: (1) (a1, b2, c3, d4; ..., d9, d10), (2) (a1, c2, b3, d4, ..., d9, d10), and (3) (b1, c2, b3, d4, ..., d9, d10), where a_i != b_i, b_i != c_i, etc. The measure of the cube is count. 1, How many nonempty cuboids will a full data cube contain? Answer: 210 = 1024 2, How many nonempty aggregate (i.e., non-base) cells will a full cube contain? Answer: There will be 3 ∗ 210 − 6 ∗ 27 − 3 = 2301 nonempty aggregate cells in the full cube. The number of cells overlapping twice is 27 while the number of cells overlapping once is 4 ∗ 27 . So the final calculation is 3 ∗ 210 − 2 ∗ 27 − 1 ∗ 4 ∗ 27 − 3, which yields the result. 3, How many

How data mining can assist bankers in enhancing their businesses is illustrated in this example. Records include information such as age, sex, marital status, occupation, number of children, and etc. of the bank?s customers over the years are used in the mining process. First, an algorithm is used to identify characteristics that distinguish customers who took out a particular kind of loan from those who did not. Eventually, it develops ?rules? by which it can identify customers who are likely to be good candidates for such a loan. These rules are then used to identify such customers on the remainder of the database. Next, another algorithm is used to sort the database into cluster or groups of people with many similar attributes, with the hope that these might reveal interesting and unusual patterns. Finally, the patterns revealed by these clusters are then interpreted by the data miners, in collaboration with bank personnel.4

Often times as researchers, we will take continuous variables and break it up into categories, typically for the purpose of grouping, or to measure an outcome measure. We usually decide on a reasonable cut-point and place the data in the appropriate category. Streiner (2002) asserts that this categorization results in lost information, reduced power of statistical tests, as well as an increased probability of a Type II error. In Streiner (2002) article, written primarily for researchers, he states that the only justified reasons for dichotomizing a continuous variable is when the distribution is highly skewed or the data shows a non-linear relationship with another variable.

Data Warehousing and Data Mining has always been associated with manufacturing companies, where sales and profit is the main driving force. Subsequently Higher Education has grown throughout the years; this growth is predominately associated with the increase of online institutions. This growth has resulted in higher education to adapt to a more business like institution (Lazerson, 2000).

Support Vector Machine (SVM) is primarily a classier method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables. For categorical variables a dummy variable is created with case values as either 0 or 1. Thus, a categorical dependent variable consisting of three levels, say (A, B, C), is represented by a set of three dummy variables: