Physics Of Theoretical And Applied Information Technology

Diabetes Mellitus is one of the major medical conditions that it presence in patients has been predicted using various types of learning algorithms such as ADAP, and which results showed a performance of 76% (recognition rate). It is evident that for medical applications, a much higher performance is invariably required.

Data mining is the process of analyzing data from different perspectives and summarizing it into useful information, this information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. (Sometimes called data or knowledge discovery)

The main objective of the educational organization is to provide quality education to its students. One way to achieve the highest level of quality in higher education is by discovering knowledge for prediction on enrollment of students in a particular course, unfriendliness model in the traditional classroom teaching, recognition of unfair means used online examination, discovery of abnormal values in the result students leaves, prediction on the performance of students and so on. Knowledge is hidden among the set of educational data and can be extract by using data mining techniques.

Many other terms are being used to interpret data mining, such as knowledge mining from databases, knowledge extraction, data analysis, and data archaeology. Data mining is one of the provoking and significant areas of research. Data mining is implicit and non-trivial task of identifying the viable, novel, inherently efficient and perspicuous patterns of data. Figure 1 represents the data mining as part of KDD process. The hidden relationships and trends are not precisely distinct from reviewing the data. Data mining is a multi-level process involves extracting the data by retrieving and assembling them, data mining algorithms, evaluate the results and capture them. Data Mining is also revealed as necessary process where bright methods are used to extract the data patterns by passing through miscellaneous data mining

Data mining is a relatively new phenomenon, therefore the number of peer-reviewed journal articles, blogs and other online sources on the topic are limited but growing rapidly. One key book, Data Mining and Analysis: Fundamental Concepts and Algorithms by Zaki and Meira Jr., takes an algorithmic approach, as the title suggests. Zaki and Meira Jr. define data mining by stating that “data mining comprises the core algorithms that enable one to gain fundamental insights and knowledge

It also helps to present the data with reduced set of samples without representing the whole data set, which reduces the complexity in space and reduction in time. Data mining interesting knowledge includes identifying the relations, differences; groups based one the similar features extracted. Data mining mainly includes the mechanism for representing the data, Specification on required information and method to search the algorithm. Representation model used to represent the underlying data and interpretability of model which interacts with human.

Data mining: Choosing appropriate data mining algorithm is the main task in this step. After getting decided with what kind of data to be used among classification, regression, clustering etc we need to consider two important steps like: Prediction and description.

Ever since I was a little child, I was fascinated by machines and the way they operate. I remember growing up with a pile of broken toys around me. My mother has always been complaining about my obsession with disassembling toys. Whenever I got any interesting automatic toy, I had felt the urge of ripping them apart to see how they worked. I attempted to reassemble them afterwards, but most of the time they end up being damaged. I remember spending day after day trying to figure out why one of my motorized cars responded only to its sound generating remote control however not to other sounds. I did not have any knowledge of sensors or actuators back then. One of my cousins came to rescue and explained it. I was very surprised, realizing an inanimate object could possess such ability. My interest in science and engineering grew exponentially after that. The idea of being an Engineer, being able to play with the laws of physics and unveiling the unknowns has always been a driving force in my student life.

[1] Jaiwei Han and Micheline Kamber “Data Mining: Concepts and Techniques”,Morgan Kaufmann Publications Second Edition,2006

Abstract— Data mining is the method of extracting the data from large database. Various data mining techniques are clustering, classification, association analysis, regression, summarization, time series analysis and sequence analysis, etc. Clustering is one of the important tasks in mining and is said to be unsupervised classification. Clustering is the techniques which is used to group similar objects or processes. In this work four clustering algorithms (K-Means, Farthest first, EM, Hierarchal) have been analyzed to cluster the data and to find the outliers based on the number of clusters. Here the WEKA (Waikato Environment for Knowledge Analysis) for analyzing the clustering techniques. Here the time, Clustered and un-clustered

Our research is to apply DM on a given data set extracted from data held in RMIS at JKUAT. The literature review on the methodology used is presented in this chapter under Section 2.4. Before this we have the definition of terms in DM given in section 2.2 defining data mining, concept of knowledge

Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to gain competitive advantage, improve processes, gain efficiency, save costs, utilizing and allocating resources optimally. Data mining tools not only analyze data from all perspectives, but also form relationships between seemingly random data into meaningful and actual information by finding correlations or patterns among dozens of fields in large relational databases which can be used to improve business and also gain intelligence which safeguard against

In every day live, the word ‘Mining’ refer to the process that discovered a small set of valuable pieces from a great deal of raw material as in mining process of gold from rocks or sand. According to [3] Data Mining, or Knowledge Discovery in Databases (KDD) as it is also known, is the process of extraction of implicit information that previously unknown and potentially useful from database. By using a number of different technical, such as clustering, data summarization, learning classification, finding dependency networks, analyzing changes, and detecting anomalies. Data Mining refers to a variety of techniques that can be used to analyses and observes database in order to find relationships or summarize the data in ways that can be put to use in different areas such as decision making, prediction and estimation and to do that there are a sequence of the process [2] . As show in figure (1.1)

Data mining is the process of ‘digging-out’ patterns from data, usually through Clustering, Classification, Regression and Association rule learning. Data mining technology can generate new business opportunities by providing:

From a practical perspective, Data Mining automates the whole process of categorizing and discovering new understandable relationship by using advanced tools and utilizing some basic understanding of statistics, machine learning and database systems. The useful accurate information we acquire after applying this process is reusable and utilized to take important steps towards increased revenue, reduced costs in retail, financial, communication, and marketing business organization. The wide range of applicability in heterogeneous domains which comprises of large volume of rich data makes Data Mining an important and challenging sector for the Data scientists.