modeling techniques. Some of the operations performed using Weka like classification, clustering etc. are discussed here. It provides implementation of various algorithms which can be applied on any kind of datasets. It has wide range of features which perfectly suits for a data mining process. This includes preprocessing of data to clean and structure the data. Then it is followed by data classification/clustering by choosing appropriate algorithm. Weka contains four different types of interface.
unstructured data which have to be evaluated. The evaluation is based on the semantic similarity between the model answer and the user answer. Different techniques are compared and a new approach is proposed to evaluate the subjective test assessment of text. Index terms: Subjective test assessment; Online examinations; Semantic Similarity; Evaluation. I. INTRODUCTION Although assessment is a tough job, but it can be helpful by making it computerized. Normally, examinations are of two types objective
proposing attribute settings based on the preferences of a customer community. Advantages: • A minimal amount of users are required, since they do not require huge amounts of data to computer recommendations, unlike collaborative technique. • Sensitive to preference changes. • Detailed qualitative preference feedback. • Transparency, since they are able to generate explanations for their recommendations. Disadvantages: • Knowledge base creation and maintenance requires much effort with a heavy human
understanding of modern NLP is the understanding of computational capabilities and logic. Elizabeth Liddy offers a less vague definition of NLP as “a theoretically motivated range of computational techniques for analyzing and representing naturally occurring texts at one or more levels of linguistic analysis for the purpose of achieving human-like language processing for a range of tasks for applications,” (2001). In Liddy’s definition, the wide range of methods, types of languages, and forms of analysis are
Work For Clustering Concept Drifting Categorical Data Authors: Raja Vaghicharla, Ravi Vemuri, Ramakrishna Rama Under guidance: Dr. Victor Shengli sheng Computer Science Department University of Central Arkansas Abstract: Data clustering is the most important technique in studying data analysis and it is also important in researching several domains regarding the analysis for which sampling has been important to improve the efficiency of clustering. However,
Data Mining in Finance 1. Introduction Data mining is used to uncover hidden knowledge and patterns from a large amount of data. In finance, there is enormous data which generates during business operations and trading activities. Extracting valuable data from them manually might be unable or spend a lot of time. As a result, data mining plays an importance role of discovering data that can be used to increase profits and evaluate risks of strategic planning and investment. The data mining methods
The paper starts off talking about SPLE (software product line engineering). SPLE refers to software engineering methods, tools and techniques for creating a collection of similar software systems from a shared set of software assets using a common means of production. Carnegie Mellon Software Engineering Institute defines a software product line as "a set of software-intensive systems that share a common, managed set of features satisfying the specific needs of a particular market segment or mission
being recorded, audio or video is not usually well accepted. Also even though video are good source of information for low-level activities, they have the drawback of not being able to scale out easily [2]. Every other sensor has its own advantages and disadvantages. Our analysis is focused on Activity prediction based on location sensors. We infer that by predicting the location, we are also predicting the activity of the user, hence in the preceding analysis we might not explicitly state activity
Introduction Nowadays, data mining and machine learning become rapidly growing topics in both industry and academic areas. Companies, government laborites and top universities are all contributing in knowledge discovery of pattern recognition, text categorization, data clustering, classification prediction and more. In general, data mining is the technique used to analyze data from multi perspectives and reveal the hidden gem behind the enormous amount of data. With the explosive growth of data collections,
speaks logically and ethically with a minute amount of figurative speech scattered in the text which creates a competent scholarly article. From the start of his article, Berry appeals to logos by using a quote from George Bernard Shaw, famous Irish playwright and critic, indicating “Progress is impossible without change” (552). Throughout the rest of the introduction, he gave the reader advantages and disadvantages on the current standing the NCAA has on the compensation of student-athletes. The argument