The economic development and promotion of a country or region is depends on several facts such as- tourism, industries, transport, technology, GDP etc. The Government of the country is responsible to facilitate the opportunities to develop tourism, technology, transport etc. In view of this, we look into the Department of Tourism to predict and classify the number of tourists visiting historical Indian monuments such as Taj- Mahal, Agra, and Ajanta etc.. The data set is obtained from the Indian Tourist Statistics which contains year wise statistics of visitors to historical monuments places. A survey undertaken every year by the government is preprocessed to fill out the possible missing values, and normalize inconsistent data. Various classification techniques under Decision Tree approach such as- Random Tree, REPTree, Random Forest and J48 algorithms are applied to classify the historical monuments places. Performance evaluation measures of the classification models are analyzed and compared as a step in the process of knowledge discovery.

The impact of social Medias such as YouTube, Twitter, and FaceBook etc on the modern world is led to huge growth in the size of video data over the cloud and web. The evolution of smart phones/Tabs could be one of the reasons for increasing in the rate of huge video data over the web. Due to the rapid evolution of web videos over the web, it is becoming difficult to identify popular, non-popular and average popular videos without watching the content of it. To cluster web videos based on their metadata into ‘Popular’, ‘Non-Popular’, and ‘Average Popular’ is one of the complex research questions for the Social Media and Computer Science researchers’. In this work, we propose two effective methods to cluster web videos based on their meta-objects. Large scale web video meta-objects such as- length, view counts, numbers of comments, rating information are considered for knowledge discovery process. The two clustering algorithms-Expectation Maximization (EM) and Distribution Based (DB) clustering are used to form three types of clusters. The resultant clusters are analyzed to find popular video cluster, average popular video cluster and non-popular video clusters. And also the results of EM and DB clusters are compared as a step in the process of knowledge discovery.

Computer applications and business administrations have gained significant importance in higher education. The type of education, students get in these areas depend on the geo-economical and the social demography. The choice of a institution in these area of higher education dependent on several factors like economic condition of students, geographical area of the institution, quality of educational organizations etc. To have a strategic approach for the development of importing knowledge in this area requires understanding the behavior aspect of these parameters. The scientific understanding of these can be had from obtaining patterns or recognizing the attribute behavior from previous academic years. Further, applying data mining tool to the previous data on the attributes identified will throw better light on the behavioral aspects of the identified patterns. In this paper, an attempt has been made to use of some techniques of education data mining on the dataset of MBA and MCA admission for the academic year 2014-15. The paper discusses the result obtained by applying RF and RT techniques. The results are analyzed for the knowledge discovery and are presented.

Data Mining is a dominant tool for academic and educational field. Mining data in education atmosphere is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational/academic database and can be used for decision making in educational/academic systems. This work demonstrates an effective mining of students performance data in accordance with placement/recruitment process. The mining result predicts weather a student will be recruited or not based on academic and other performance during the entire course. To mine the students’ performance data, the data mining classification techniques such as – Decision tree- Random Tree and J48 classification models were built with 10 cross validation fold using WEKA. The constructed classification models are tested for predicting class label for new instances. The performance of the classification models used are tested and compared. Also the misclassification rates for the classification experiment are analyzed.

Now a days, the Data Engineering becoming emerging trend to discover knowledge from web audio-visual data such as- YouTube videos, Yahoo Screen, Face Book videos etc. Different categories of web video are being shared on such social websites and are being used by the billions of users all over the world. The uploaded web videos will have different kind of metadata as attribute information of the video data. The metadata attributes defines the contents and features/characteristics of the web videos conceptually. Hence, accomplishing web video mining by extracting features of web videos in terms of metadata is a challenging task. In this work, effective attempts are made to classify and predict the metadata features of web videos such as length of the web videos, number of comments of the web videos, ratings information and view counts of the web videos using data mining algorithms such as Decision tree J48 and navie Bayesian algorithms as a part of web video mining. The results of Decision tree J48 and navie Bayesian classification models are analyzed and compared as a step in the process of knowledge discovery from web videos.

Nowadays YouTube becoming most popular video sharing website, and is established in 2005. The YouTube official website is providing different categories videos including Science and Technology, Films and Animation, News and politics, Movies, Comedy, Sports, Music etc. Each video hosted in website such as YouTube have its own identity and features. The identity and features of each video can be described by web video metadata objects such as- URL of each video, category, length of the video, rating information, view counts, comment information, key words etc. Using extracted web video metadata objects, we present an in-depth and systematic clustering study on the metadata objects of YouTube videos using Expectation Maximization (EM) and Density Based (DB) clustering approach. Distinct web video metadata object clusters are formed based on different category of web videos. The resultant clusters are analyzed in depth as a step in the KDD process.