Big data processing is recently becoming growingly salient in modern age due to the constant growth of data generated by diverse fields. However the effectiveness of discovering patterns for knowledge discovery is unclear. Knowledge Discovery concepts play a major role in data analysis.In the overall scheme of knowledge discovery, Data Mining techniques has been found unexpectedly to usually devoted to extraction of information from structured databases and data warehouse.Text Data Mining techniques, on the other hand, are dedicated to extraction of information from unstructured textual data e.g electronic texts on web. Although there are various researches being carried out in the area of conventional data mining, little has been carried …show more content…
Data Mining looks for patterns within data in databases. It aid extraction of useful information from various databases(Data Warehouses). Data mining works with large amounts of data. Because of the large amounts, the knowledge hidden in the data is not visible at first sight and it must be discovered. It implies that at the beginning of the process the knowledge is not known. The identified patterns and relationships can be new and surprising.
Data in data mining means structured, relational data[6]. Text mining works with unstructured data— texts. Text mining is extraction of useful information from text data it is also known as text data mining or knowledge discovery from textual databases.
Text mining is a variation on a field called data mining that tries to find interesting patterns from large databases.Few researches have been carried out on text data mining[8]. On the basis of these researches information retrieval techniques such as text indexing,text classification and text summarization methods have been developed to handle unstructured documents(Soundararajan et al,2014).
1.0.1 TEXT DATA MINING AND UNSTRUCTURED
Predictive and Text Analysis on Big Data – Being able to forecast data and analyse critical information for the company.
There are two types of data. They are unstructured and multi-structured. Unstructured data comes from information that isn’t organized or easily interpreted by traditional databases or data models. This is usually in text format.
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
There is a debate between the benefits and potential informational privacy issues in web-data mining. There are large amount of valuable data on the web, and those data can be retrieved easily by using search engine. When web-data mining techniques are applied on these data, we can get a large number of benefits. Web-data mining techniques are appealing to business companies for several reasons [1]. For example, if a company wants to expand its bu...
A database is a structured collection of data. Data refers to the characteristics of people, things, and events. Oracle stores each data item in its own field. For example, a person's first name, date of birth, and their postal code are each stored in separate fields. The name of a field usually reflects...
Information Retrieval (IR) is to represent, retrieve from storage and organise the information. The information should be easily access. User will be more interested with easy access information. Information retrieval process is the skills of searching for documents, for information within documents and for metadata about documents, as well as that of searching relational databases and the World Wide Web. According to (Shing Ping Tucker, 2008), E-commerce is rapidly a growing segment in the internet.
String matching is very popular and an important technique which is widely used in information retrieval, text processing and in many other web related, and software applications. String matching simply states that a pattern string should be matched to the text string. String matching is used to determine the position of a pattern string in a text string and to find all or nearest strings correspond to pattern string. String matching is a technique, which is applied in real life applications as well. As the necessities arise, the need for technology is come around. It is applied in string matching also. As we know that many people are now aware of world of computers, internet and information technology, and because a large amount of data are
R. Agralwal, T.Imielinski, and A.Swami. Mining associations between sets of items in large databases. In P.Buneman and S. Jajodia, editors, SIGMOD93, pages 207-216, Washington, D.C, USA, May 1993
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
THURAISINGHAM, BHAVANI. (2003). Web Data Mining and Applications in Business Inteligence and Counter-Terrorism.Taylor & Francis.http://www.myilibrary.com?id=6372.
... applied on different Domain data sets and sub level data sets. The data sets are applied on Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms, I got 60-70% of accuracy. The above is also applied for the Unigrams of Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms achieved an accuracy of 65-75%. Applied the same data on proposed lexicon Based Semantic Orientation Analysis Algorithm, we received better accuracy of 85%. In subjective Feature Relation Networks Chi-square model using n-grams, POS tagging by applying linguistic rules performed with highest accuracy of 80% to 93% significantly better than traditional naïve bayes with unigram model. The after applying proposed model on different sets the results are validated with test data and proved our methods are more accurate than the other methods.
Big data originated with web search companies that encountered problems with querying large amounts of both structured and unstructured data. With regard to its background, “big data came into being when web search companies developed ways to perform distributed computing on large data sets on computer clusters” Floyer (2014: 1). Big data then spread to enterprises due to their adoption of developing, processing and dissemination of data.
The data mining process will use the mapping function which involved the decision tree and also the neural network to develop. It needs the web server and the database server to be constructed in an operating database to record the browsing route of the users. The data mining will use to identify the user’s information and classify them into different classes using decision tree.