10 Facts on Data Mining for a Research Project

10 Facts on Data Mining for a Research Project Data mining is the study of analyzing large amount of data and developing a pattern which can be used for several suitable purposes. If you want to write a research project on this topic, then there is a lot of potential, and we are going to help you kick-start your brainstorming process. There are various terminologies and studies synonymous to the process of data mining. One feature which is common is that it’s an application based system, meaning the results are compared with the desired output and if it’s not up to the mark, the process is then repeated again. Data mining has significantly improved in the past four years as processors have been improved. Revolutionizing the marketing industry, top analysts have been using this technique to reach their ultimate customers online and offline. Here are 10 facts on data mining which you can use for your research project: Before data mining can begin, proper data representation needs to be decided which can make data analysis possible. In data mining, there is a problem of complex data such as sequences and images which requires the establishment of a set of attributes which can then be used in multivariate vectors. Once that’s established, we refine the statistics through kernel matrices, vector kernels and basic kernel operations for data analysis. Various statistical methods can be used to analyze numeric attributes during data mining. Central location, dispersion and linear dependence is measured all while keeping the probabilistic, geometric and algebraic representation of data matrix in mind. There are various ways of analyzing numeric attributes. The Univariate Analysis focuses on one, the Bivariate Analysis focuses on two and the Multivariate Analysis focuses on all numeric attributes simultaneously. Furthermore, if the attributes are two or more and the values are drastically different, then there is the process of Data Normalization. When the process of data mining is being performed, the data being processed is extremely high dimensional. This is due to the overwhelming amount of attributes, which are in the number of thousands. This is where the analysis enters the domain of high dimensional space or hyperspace, as the factors comprising the analysis stop behaving like normal geometry. Mathematically speaking, high dimensional volume is calculated through the analysis of high dimensional objects. This is where data mining becomes complex because calculations need hypercube, the volume of thin hypersphere shell, diagonals inside hyperspace and the density of multivariate normal. Some of the characteristics of the data inside hyperspace are counterintuitive. The center of space in high dimensions is free of exact points, also there is an accelerated establishment of orthogonal axes. This leads to the problem of unstable data mining of high-dimensional data. That’s why it becomes important to reduce the dimensionality while making sure that the data inside the matrices remain intact. This can be achieved through the processes of principal component analysis, kernel principal component analysis and singular value decomposition. Frequent pattern mining is one of the most important analysis in data mining. This method is used to improve browsing experience and is being implemented by Google and various ecommerce sites. Large variables from weblogs are inserted in custom designed algorithms to know which pages are visited more frequently. This helps web designers and search engines to optimize their systems to get more organic clicks. It also helps online shops know which products sell, making sure they are prioritized in searches. Data mining is the analysis of data of all types, but this creates an efficiency problem because every data has its own identification, decoding technique and other characteristics. This problem is solved through the process of cluster analysis. Clustering divides similar information into groups. The similarity can be general or operation specific; this means that to efficiently process all data to get the desired results, there can be hundreds of clustering algorithms in a single data mining system. Clustering can be based on representation, hierarchy or density. Classification in data mining is the process of estimating a class label if a point is unlabeled. In this case, probabilistic classification is performed, such as the Bayes Classifier which incorporates the Bayes Theorem to effectively predict the class. Its objective is to predict the joint probable function of a particular class. The class itself is designed through the multivariate normal distribution. Another method called the Naive Bayes Classifier process; which takes the attributes as independent, is also very reliable for several applications. Another probabilistic method to predict an unlabeled point is called the decision tree classifier. It’s a method to design a tree model based on observations about a particular variable to understand its target value. Here, the targeted variable in a tree model can take a finite set of values. In a tree structure, the leaves represent class labels and the branches represent coinciding instances that lead up to class labels. If the targeted variable is taking real numbers, continuous values, then it’s called a regression tree. Support vector machines, also known as SVMs, are yet another classification process which is primarily based upon maximum margin linear discriminants. The purpose of these machines is to ascertain the most efficient and perfect hyperplane can maximize the space between the classes. Another method to perform this function is to utilize the kernel to find the most efficient nonlinear decision boundary in between the classes, which is in correspondence with a few high-dimensional nonlinear spaces. In order to optimize a search engine and perform efficient data mining, automatic summarization algorithms are put in place. These algorithms are able to summarize large amounts of texts by keeping the length, syntax and writing style in consideration. This makes it possible to analyze large texts as these texts can now be represented by a small subset of data which has enough details. This brings us to the end of an informative fact guide about data mining. Data mining makes it possible for machines to analyze large quantities of data to understand a user pattern. If this wasn’t enough for you to grasp onto a topic, then read our next piece which is 20 data mining project topics for you to research and don’t forget to check our complete guide on this academic genre and the subject. Overall, if you are looking for a professional research project writing service to get help with your Data mining research paper you should visit our company. References: Aggarwal, C. C. (2015). Data Mining: The textbook. Cham: Springer. Deshpande, V. K. (2015). Predictive Analytics and Data Mining: Concepts and Practice with Rapidminer. Morgan Kaufmann. Han, J., Kamber, M., Pei, J. (2011). Data mining: Concepts and techniques concepts and techniques. San Francisco: Morgan Kaufmann In. Linoff, G., Berry, M. J. (2011). Data Mining Techniques: For Marketing; Sales; and Customer Relationship Management; Third Edition. John Wiley Sons Incorporated. Nisbet, R., Elder, J. F., Miner, G. (2009). Handbook of statistical analysis and data mining applications. Amsterdam: Academic Press/Elsevier. Provost, F. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. Russell, M. A. (2013). Mining the Social Web: Data Mining from Facebook, Twitter, and LinkedIn, Google , GitHub, and More (2nd Edition). OReilly Media.