Data Mining Thesis Topics:

We develop Data mining Thesis Topics based on information retrieval, pattern discovery, clustering classification and association rule mining.Data mining is defined as process of extracting valid information from database. More students in computer science engineering students are interested to do final year projects in data mining.  Under data mining thesis , we develop more projects such as fraud detection, frequent pattern generation, market analysis, similarity text classification and location based recommendation system. Main aid of data mining projects is to overcome the existing application problem and implement an innovative idea to speed the mining process. In future micro marketing and advertisement companies are enhance their quality of services by data mining thesis analysis. Our team develops more data mining applications with enhancing classification algorithms to support long term analytical process in medical and atomic research data.

Data Mining  Thesis Application:

We listed the applications of data mining thesis  are:

  • Credit Card Companies.
  • Retail and Marketing Applications.
  • Customer Life Cycle.
  • Medical Applications.
  • Tele Communication Industry.
  • Insurance Companies.

Data Mining and Ware Housing: Data ware house gather multiple sources and load data into database which placed in an enterprise area. Data mining also extracts data from data warehouse. Major benefits of adopting data ware house is mining when data is already present then is no need of data analysis process. Both OLAP and data mining are differs based on Hypothetical pattern evaluation.

Data Mining and OLAP (On-Line Analytical Processing): OLAP referred as detective process which produces hypothetical patterns and relationship. It analyzes data mining tools and finds risk factor in that tool.


 Data Mining Terminology:

Every data mining applications deals with some technical factors for every processing such as clustering, regression and classification.

  • Clustering:

In this process we split data base into multiple groups to identify the difference among groups. We perform clustering process based on some attribute values. For clustering process data mining ensure various type of algorithm such as expectation maximization algorithm, K- means, single leakage clustering, fuzzy c means and DBSCAN algorithm.

  • Classification:

It is the process of identifying group of data feature by extracting valuable patterns. 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. We provided the classification algorithm such as neural networks, support vector machine, instance based learning, Bayesian network, naïve bays and decision tree.

  • Regression:

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.

Data Mining Algorithms:

We listed some data mining algorithms and implemented in IEEE papers are:-

  • Multivariate Adaptive Regression Spines (MARS):

To resolve CART problem, we developed MARS which replace discontinues node with another transaction node in decision tree to enhance high order transaction.

  • Logistic Regression:

It is the general form of linear regression model used to predict binary values from multi class variables.

  • Decision Tree:

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 and memory based reasoning (MBR):

K- Nearest neighbor connect nearest neighbor to clustering area and create decision about which class to place in new class or neighbor. K-NN model are easy to understand than other clustering algorithm.

  • Rule Induction:

In this method we derive various numbers of rules for classification. Decision tree generate rule for whole transaction and rule indication generate individual rule for each transaction.

  • Discriminate Analysis:

It is an oldest classification method in data mining. It is used to classify very sensitive data.