Tech thesis topics in computer science is required for academic post graduate students. PHIL computer science, information technology, communication, networking department students can be benefit for this topics. Tech thesis topics in computer science in classified to many divisions, guidance is provided to all kinds regarding M.
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.
OLAP referred as detective process which produces hypothetical patterns and relationship. It analyzes data mining tools and finds risk factor in that tool. Every data mining applications deals with some technical factors for every processing such as clustering, regression and classification.
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.
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. 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. We listed some data mining algorithms and implemented in IEEE papers are: 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 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. 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.
It is an oldest classification method in data mining. It is used to classify very sensitive data.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.e World Wide Web. The information for web mining is collected through browser activities, page .
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