Data Mining is a relatively new field has a bright scope now as well as in future. The scope of this field is high due to the fact that markets and businesses are looking for valuable data by which they can grow their business. Data mining as a subject should be mandatory in computer science syllabus. As earlier said data mining is a good topic for an M. Tech thesis.
Students can go for deep research to have a good content for their thesis report. Data Mining finds its application in Big Data Analytics. Following is the list of latest topics in data mining for final year project, thesis, and research:. Web Mining — Web mining is an application of data mining for discovering data patterns from the web.
Web mining is of three categories — content mining, structure mining and usage mining. Content mining detects patterns from data collected by the search engine. The data collected through web mining is evaluated and analyzed using techniques like clustering, classification, and association.
It is a very good topic for the thesis in data mining. Predictive Analytics — Predictive Analytics is a set of statistical techniques to analyze the current and historical data to predict the future events. The techniques include predictive modeling, machine learning, and data mining. In large organizations, predictive analytics help businesses to identify risks and opportunities in their business.
Both structured and unstructured data is analyzed to detect patterns. Predictive Analysis is a lengthy process and consist of seven stages which are project defining, data collection, data analysis, statistics, modeling, deployment, and monitoring. It is an excellent choice for research and thesis. It provides powerful data mining algorithms to assist the data analysts to get valuable insights from data to predict the future standards.
It helps in predicting the customer behavior which will ultimately help in targeting the best customer and cross-selling. SQL functions are used in the algorithm to mine data tables and views. It is also a good choice for thesis and research in data mining and database. Clustering — Clustering is a process in which data objects are divided into meaningful sub-classes known as clusters. Objects with similar characteristics are aggregated together in a cluster. There are distinct models of clustering such as centralized, distributed.
In centroid-based clustering, a vector value is assigned to each cluster. There are various applications of clustering in data mining such as market research, image processing, and data analysis. It is also used in credit card fraud detection.
Text mining — Text mining or text data mining is a process to extract high-quality information from the text. It is done through patterns and trends devised using statistical pattern learning. Firstly, the input data is structured. After structuring, patterns are derived from this structured data and finally, the output is evaluated and interpreted.
The main applications of text mining include competitive intelligence, E-Discovery, National Security, and social media monitoring. It is a trending topic for the thesis in data mining. Fraud Detection — The number of frauds in daily life is increasing in sectors like banking, finance, and government. Accurate detection of fraud is a challenge.
Data mining techniques help in anticipation and detection of fraud. Data mining tools can be used to spot patterns and detect fraud transactions. Through data mining, factors leading to fraud can be determined. The result can be shared for scientific research. The interactive analysis of data can be done on the cloud. It will leverage the existing interface.
Graph Mining — It is an application of data mining to extract useful patterns from the graphs. The underlying data can be used for classification and clustering. The application of graph mining includes biological network, web data, cheminformatics and many more. It is one of the good topics in data mining for thesis and research.
Fuzzy Clustering — Fuzzy Clustering is a type of clustering in which a single data point can be a part of more than one cluster. In non-fuzzy clustering, a data point belongs to only one distinct cluster. Fuzzy Clustering finds its application in bioinformatics, image analysis, and marketing.
Fuzzy Clustering employs k-means algorithms to solve various complex computation problems. It is a very challenging thesis topic in data mining. Domain Driven Data Mining — It is a methodology of data mining to discover actionable knowledge and insight from complex data in a composite environment.
Data-driven pattern mining faces challenges in the discovery of actionable knowledge from databases. How are data mining technologies being used to find whether or not proprietary information is being stolen and revised in a manner that escapes detection from traditional scanners? How did the poor application of data mining technologies at the time lead to the U. What are the major internet domains now use advanced forms of data mining to either rank higher on search engines or to find what kind of internal content attracts readers?
In what ways to financial investment firms utilizing data mining techniques to predict market behavior? Can computer science technologies be applied to new methods of research currently being explored to measure the potential effectiveness of cancer treatments? Can data mining technologies be used to assist consumers in the way they make purchases?
If so, would major corporations be against their use if it affects profits? Why are some industries prone to monopolization because of the incorporation of computer science technologies that have yet to go to market?
We use this pattern to differentiate previous data and predicted data. By the classification process we split given data into test and training data set. To correct the error in derived data we use test data. To match previously unseen records we use training data set.
In regression process we use standard statistical techniques for linear projects. Regression and classification are using same model type as classification and decision tree algorithm. To resolve CART problem, we developed MARS which replace discontinues node with another transaction node in decision tree to enhance high order transaction. It is the general form of linear regression model used to predict binary values from multi class variables.
To model training data set it uses tree structure. Decision tree use attribute and class value to construct tree. Classification and regression algorithm create two branches at every node. Inner node contains attribute value and leaf node contains class value. We refer decision tree as binary tree which used in data mining projects to examine data and relationship based on algorithm such as Quest, CART and CHAID under data mining environment.
K- Nearest neighbor connect nearest neighbor to clustering area and create decision about which class to place in new class or neighbor. Sequential Analysis — Sequential analysis is a technique that discovers and identifies similar patterns, events, and trends in transactional data over a certain period of time.
There are various real-life examples of data mining from everyday life. The most common example for this is cross-selling by e-commerce sites based on the searches made by the customer on the web. Another example for this is the loyalty card programme run by various stores and markets to gather valuable customer information.
Fraud detection, particularly in the field of telecommunication and card sale service, is another example for this. Data mining helps in determining duration, location and time of the call in case of fraud calls. Data mining is used in wide range of areas from telecommunication to financial areas. It is also being taught as a subject in various colleges as a part of the curriculum, particularly in computer science. For masters students, this is a very good thesis topic as well as for research.
Numerous agencies are available over the Internet that will provide thesis writing assistance and help for data mining. It is a relatively new technology and yet to reach a wider audience. A lot of data is generated in medical science every day which needs to be managed.
Data Mining is useful in this case for extracting valuable information from this data thus generated. Data Mining is helpful in medical science to:. Data Mining can be used to analyze customer behavior by tracking his different purchases and daily activities. We can get information about how much does a customer spends using his credit card and which product he usually buys.
Data Mining is very helpful, particularly in marketing and sales business. Through data mining, marketing and sales enterprises can make offers to customers based on their purchases and also on what product he usually searches. Data Mining also finds its application in the field of science and engineering for the development of new products like sensor devices and pattern recognition system. Data Mining also finds its application in Machine Learning, pattern recognition, database management and artificial intelligence.
Web Mining is an application of Data Mining and an important topic for research and thesis. It is a technique to discover patterns from WWW i. The information for web mining is collected through browser activities, page content and server logins. It is a very good area for master thesis data mining.
There are three types of Web Mining:. It is a technique to extract usage patterns from Web Data. These patterns are used for understanding the needs of Web-based applications. Web usage mining can also be classified according to the following type of data:.
Web Content Mining refers to the extraction of useful information and data from Web Page content. For retrieving information from the web page intelligent tools like web agents are used. Intelligent Systems are created which involve this agent-based approach. In this technique, graph theory is used for analyzing the node and structure of the website. It can be classified into two different types :. It is an important field of Data Mining.
It refers to the process of extracting valuable information from text and is also referred to as text analytics. This high-quality information is extracted through patterns and methods like statistical pattern learning. It is another good area for the Ph. In Text Mining, input data is structured and patterns are derived from this structured data.
There are various research areas and thesis topics in the field of text mining. For any thesis help on data mining, contact us. Techsparks provides thesis guidance in data mining. Tags: data mining , data mining process and techniques , data set , education , M. Search for:. Reviews Contact Us Get Directions. Data Mining — its process and techniques. Techsparks provide the following two guidance packages:. Get a Quote.