Data Mining And Machine Learning

Data mining has been come increasingly easier in recent years. It cannot be done manually because it requires applying mathematics, statistics, and pattern matching to large amounts of data[iv] but advances in computer hardware and software have made data mining on a large scale a reality. This has

In its infancy, data mining was as limited as the hardware being used. Large amounts of data were difficult to analyze because the hardware simply could not handle it [1]. The term "data mining" first began appearing in the 1980 's largely within the research and computer science communities. In the 1990 's it was considered a subset of a process called Knowledge Discovery in Databases of KKD [1]. KKD analyzes data in the search for patterns that may not normally be recognized with the naked eye. Today however, data mining does not limit itself to databases,

This paper will look at some of the ‘Big Data’ being implemented today. Regardless of ow anyone feel, ‘Big Data’ s a thing that is not going away. This paper will look at Video and Image Data, Audio Data, Textual Data, Managerial Accounting.

This research paper highlight the importance and need of data mining in the age of electronic media where large amount of information and consolidated database is readily available. This seemingly useful information can unearth some mind-blowing statistics and predict the future trends with relative ease through use of data mining techniques which can benefit the businesses, start-ups, country and individual alike. However, since data mining is effective in bringing out patterns, alerts, correlation and association through complex algorithms and analysis, it has, over the past few decades proved to be a useful

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.

Technology has impacted the world in a huge way and one of the areas that have been greatly impacted is the world of technology. More information has been made available in the world through various technological channels and this has led to a demand of data bases which can handle this big data and make it available to various users. Companies such as Facebook, Google and Amazon have been in the front line as

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.

This case study is based on a China based university which comprises of 8323 students & it provides higher education in various fields. It is because the tuition fees is higher education is higher in China. The reform of higher education is depleting from universities in China. It is because of tuition fees increasing every year in china, many students cannot afford it which is major concern for their lifestyle. Due to this concern, government helps this university to build support system for

Data mining and knowledge discovery is the name frequently used to refer to a very interdisciplinary field, which consists of using methods of several research areas to extract knowledge from real-world datasets. There is a distinction between the terms data mining and knowledge discovery which seems to have been introduced by [Fayyad et al.1996].the term data mining refers to the core step of a broader process, called knowledge discovery in database. Architecture of data mining structure is defined the following figure.

data is also Growing. It has resulted large amount of data stock in databases , depot and other repositories . therefore the Data mining comes into model to explore and analyses the databases to extract the interesting and previously obscure patterns and rules well-known as association rule mining

Data mining is not another hype. Instead, the need for data mining has arisen due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. Thus, data mining can be viewed as the result of the natural evolution of information technology.

Abstract— Data mining is a logical process that is used to search through large amount of data in order to find useful data [2].There are many different types of analysis that can be done in order to retrieve information from big data. Each type of analysis will have a different impact or result. Which type of data mining technique you should use really depends on the type of business problem that you are trying to solve.

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

Data mining consists of analysing huge sets of data and extracting relevant information and data patterns. Companies often have very large data sets that needs to be analysed for many different purposes. Initially this was a hard task to accomplish because of limitations in computing power. However, computer technology has accelerated so fast in recent past that analysing large volumes of data has become possible. Companies use these analysis results for

From a practical perspective, Data Mining automates the whole process of categorizing and discovering new understandable relationship by using advanced tools and utilizing some basic understanding of statistics, machine learning and database systems. The useful accurate information we acquire after applying this process is reusable and utilized to take important steps towards increased revenue, reduced costs in retail, financial, communication, and marketing business organization. The wide range of applicability in heterogeneous domains which comprises of large volume of rich data makes Data Mining an important and challenging sector for the Data scientists.