Data Mining, Harmony Achieved

The big data analytics deals with a large amount of data to work with and also the processing techniques to handle and manage large number of records with many attributes. The combination of big data and computing power with statistical analysis allows the designers to explore new behavioral data throughout the day at various websites. It represents a database that can’t be processed and managed by current data mining techniques due to large size and complexity of data. Big data analytic includes the representation of data in a suitable form and make use of data mining to extract useful information from these large dataset or stream of data. As stated above the big data analytics has recently emerged as a very popular research and practical-oriented framework that implements i) data mining, ii) predictive analysis forecasting, iii) text mining, iv) virtualization, v) optimization, vi) data security, vii) virtualization tools for processing very large data sets. In the implementation of big data applications, new data mining techniques and virtualization are required to be implemented due to the volume, variability, forms and velocity of the data to be processed. A set of machine learning techniques based on statistical analysis and neural networking technology for big data is still evolving but it shows a great potential for solving a big data business problems. Further, a new concept of in-memory database for enhancing the speed for analytic processing is further helping

We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.

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

Provide brief but complete answers. One page maximum (print preview to make sure it does not exceed one-two pages).

The need to find a way to handle big data leads to data mining. Most researchers defined data mining similarly to Swain (2016). Swain described data mining as using technological processes to analyze big data and find unsuspected relationships among variables for future use. He continues to state that data mining technologies can find value from billions of gigabytes of data gathered from various sources. Huang, Lu, & Duan (2012) add to Swain’s definition by noting that, as opposed to typical statistical studies, data mining uses computational methods that allow the study to look

Data mining is the process of analyzing data from different perspectives and summarizing it into useful information, this information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. (Sometimes called data or knowledge discovery)

The support vector machine classifier creates a hyper plane or multiple hyper planes in high dimensional space that is useful for classification, regression and other efficient task. SVM have a many attractive features due to this gaining popularity and have promising empirical performance. SVM build hyper plane in original input space to separate the data points. Some time that is difficult to perform separation of data points in original input space, so to make separation easier the original finite dimensional space mapped into new higher dimensional space. SVM is works on principal that data points are classified using a hyper plane which maximizing the separation between data points and the hyper plane is constructed with the help of support vectors

With rapid advancements in the technology, new concepts are hitting the industry and it is redefining itself over a course of time. The data mining is one of its kind to improvise the lives of people. Data mining uses techniques which are helpful in finding out the different forms of data. The data mining is closely related to the database technology. Almost every industry takes the help of the datamining to grow in their respective fields. For instance, stock management, quality control, risk management, fraud detection, marketing and analysis of investments. It has its applications ranging from finding the molecule structure of the gene to identifying a robbery at an international level.

Data analysis and predictive modeling with all its scope, has already reached to the very center of our operative organization. Currently, to improve the efficiency of operations, all companies, government bodies, institutions or industries are making decisions guided by predictive stats. In addition, with more data, enhanced hardware, increased knowledge, and analytical advancement, we see extensive promise towards predictive analytics. For example, predictive analytics based on our biometric values differentiate from an imposter while starting engine of our car, Music app plays song depending upon your mood level, etc. Of course, these specifics are just speculations; predictive analytics yet need to be explored fully. However, we are confident to see growth and improvement in the field.

Data mining has drastically altered how businesses are being operated, especially in the online domain. This paper attempts to review the massive growth of data mining applications to improve the profitability of e-commerce ventures. As consumers increasingly tend to make their purchases online and with many companies investing heavily on improving their web presence, it is only natural that the whole process of collecting and mining web data has become instrumental in determining a company’s bottom line. What started off as a simple analysis of user behavior and online transactional data has now transformed into a massive, multidisciplinary knowledge discovery process involving huge data sets, algorithms, statistical techniques and analytics to glean new information. The article focuses on some data mining applications unique to e-commerce ventures and how they are being utilized in the current scenario.

Based on these trends, large amount of data are being gathered and stored in databases, and data warehouses. The huge volume and fast pace made the power of data much stronger than what we expected, with lots of potential waiting us to maintain, explore and make decisions about. Using the efficient way to analyze the most helpful and valuable data, as well as to find out the hidden data is becoming urgent and important. Because of these needs, data mining started to be used as a helpful technology, and plays an important role under today’s studying and working environment.

Traditionally Data Mining is a process of extracting useful knowledge from a large volume of data set. The generated knowledge is applied and used for the most of the applications in all the areas such as science, engineering, business, research, social, health, education, entertainment and all. As all the

[1] Jaiwei Han and Micheline Kamber “Data Mining: Concepts and Techniques”,Morgan Kaufmann Publications Second Edition,2006

From Figure 2, it is clear that the values predicted by bagging, boost and neural network methods came very close to actual values. On the other hand, the SVM with radial basis function (RBF) kernel and SVM with 3 order of polynomial model generated a horizontal straight line, i.e. did not change at all. Similar trends (shown in Figure 4) were observed for the rest of the parameter sets. This shows that SVM has a poor performance in general as compared to other methods.

Abstract— Classification problems often have a large number of features in the data sets, but only some of them are useful for classification. Data Mining Performance gets reduced by Irrelevant and redundant features. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main objectives are maximizing the classification performance and minimizing the number of features. Moreover, the existing feature selection algorithms treat the task as a single objective problem. Selecting attribute is done by the combination of attribute evaluator and search method using WEKA Machine Learning Tool. We compare SVM classification algorithm to automatically classify the data using selected features with different standard dataset.