Data Mining Soltions

Coenen at the University of Liverpool defines data mining “as a set of mechanisms and techniques, realized in software, to extract hidden information from data" [1]. Data mining ventures to make it easier for humans to make decisions by making data easier to manage and patterns easier to find. With more information, humans can make better business and financial decisions. The term was first written down in the 1980’s and has sense then become one of the fastest growing components of the computer science world. Data Architects make an average of $105,000 every year, entry-level positions starting at $75,000 a year, according to PayScale.com. These jobs and skills are in high demand and in large part there is still a lot to be explored in this area of research. And as technology proceeds to move forward, the amount of data grows, and more advanced tools are needed for it to be useful.

Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any business in today’s market. Every day these facilities store large amounts of data to improve increased revenue, reduction of cost, customer behavior patterns, and the predictions of possible future trends; say for seasonal reasons. Data

Decision tree models are explanatory models, which are English rules so they are easy to evaluate and understand by people. The decision tree model is considered as a chain of rules that classify records in different bins or classes called nodes [1]. Based on the model 's algorithm, every node may have two or more children or have no child, which is called in this case leaf node [1]. Building decision tree models requires partitioning the pre-classified dataset into three parts,

little or no control over the data gathering process, with data often being collected for some

Data mining is the process of analysing data to discover meaningful patterns within the data resulting in extracting useful information that may have not been discovered yet. Data mining borrows techniques from a variety of fields such as statistics, machine learning and artificial intelligence. Because of its usefulness, data mining has been used in a range of industries such as, banking, telecommunications, retail, marketing, and insurance.

Abstract: Data mining algorithms determine how the cases for a data mining model are analyzed. Data mining model algorithms provide the decision-making capabilities needed to classify, segment, associate and analyze data for the processing of data mining columns that provide predictive, variance, or probability information about the case set. With an enormous amount of data stored in databases and data warehouses, it is increasingly important to develop powerful tools for analysis of such data and mining interesting knowledge from it. Data mining is a process of inferring knowledge from such huge data. Data Mining has three major components Clustering or Classification, Association Rules and Sequence Analysis.

A Bottled Water survey done by Mintel, in spring of 2015, found mid-priced brands show the highest market penetration. 68% of bottled water drinkers surveyed by Mintel say that they usually drink mid-priced brands. More than half of those surveyed said they usually drink value brands, showing a definite consumer preference towards mid-and value-priced brands on the market. Only 13% of those surveyed drank Premium water. (Mintel Group Ltd. 2015)

Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - 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.

With the advent of machine learning and its potential in getting best out of any application, even the data mining played the game of harnessing the power of machine learning. Needless to say, SVM is one of the very powerful and revolutionary algorithms in the field of machine learning due to its efficiency in classifying. In this report, my concentration mostly lies in discussing the applications of SVM in Data mining and analyzing the performance. Data mining is very important and essential technique in the field of analytics. The principle being extracting use full information from a massive data source and using it as an input for improvement or development. When we have a huge amount of data and equally less amount of information, data mining is one technique that enables to get better information out of the data. However, it 's not very easy to do the analysis part on huge datasets, and hence machine intelligence is introduced into the field of data mining.

A data cube allows data to be modeled and viewed in multiple dimensions. It is defined by dimensions and facts.

This is the introduction of the paper stated, “A survey of data mining and social network analysis based on anomaly detection techniques” which is authored by Ravneet Kaur and Sarbjeet Singh in Egyptian Informatics Journal in 2016. The paper first introduces the different meanings given by authors on “anomaly” and “outliers” which derive a behavior abnormal from the normal occurrence.

Knowledge discovery is a paramount process in data mining wherein data can be analyzed from divergent perspectives and summarized for future use. One of the most widely used data mining process is association rule mining. Association mining rules are further classified by observing data for patterns that are present and by consuming the criteria for analyzing the support and confidence to identify the most closely relatedinterlinked processes. In association rule mining rules are provoked and paramount relationships recognizedby analyzing data for pattern exploiting criteria support and confidence. As users have withdrawn resources for determining the results in practice, an algorithm that is being used to mine the top-k association rules

Data Mining is the non-trivial extraction of potentially useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. There are various research domains in data mining specifically text mining, web mining, image mining, sequence mining, process mining, graph mining, etc. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases

Data mining is the process of extracting hidden information from the large data set. Data mining techniques makes easier to predict hidden patterns from the data. The most popular data mining techniques are classification, clustering, regression, association rules, time series analysis and summarization. Classification is a data mining task, examines the features of a newly presented object and assigning it to one of a predefined set of classes. In this research work data mining classification techniques are applied to disaster data set which helps to categorize the disaster data based on the type of disaster occurred in worldwide for past 10 decade. The experimental comparison has been conducted among Bayes classification algorithms (BayesNet and NaiveBayes) and Rules Classification algorithms (DecisionTable and JRip). The efficiency of these algorithms is measured by using the performance factors; classification accuracy, error rate and execution time. This work is carried out in the WEKA data mining tool. From the experimental result, it is observed that Rules classification algorithm, JRip has produced good classification accuracy compared to Bayes classification algorithms. By comparing the execution time the NaiveBayes classification algorithm required minimum time.

There are growing researches in data mining as a part of education. This new developing field, called Educational Data Mining, concerns with creating techniques that find information from data originate from educational situations. The data can be collected structure verifiable and operational data dwell in the databases of educational establishments. The understudy data can be close to home or scholastic. Additionally it can be gathered from e-learning frameworks which have a vast measure of data utilized by mostly organizations. Educational data mining utilized numerous strategies, for example, decision trees, neural systems, k-nearest Neighbor, Naive Bayes, help vector machines and numerous