scholarly journals Design and Implementation of Hybrid Algorithm for e-news Classification.

2013 ◽  
Vol 12 (1) ◽  
pp. 3178-3186
Author(s):  
Harneet Kaur ◽  
Kiran Jyoti
Keyword(s):  
Data Mining ◽  
Hybrid Algorithm ◽  
Intelligent System ◽  
Large Data ◽  
Online News ◽  
Data Sets ◽  
Online Newspapers ◽  
Use Of Data ◽  
Internet News ◽  
Use Of Internet

Data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. As the use of internet is increasing day by day and with the advancement of internet news also publish online. So to handle this bulk amount of news various data mining techniques for classification had been used. In this paper we are using an intelligent system based on Hybrid algorithm (HMM, SVM and CART) for e-news classification. An intelligent system is designed which will extract the online news and then will find out category and subcategory wise news. System involves four main stages: a) Keyword Extraction b) Implementation of Hybrid Algorithm (HMM, SVM and CART). Data have been collected for experimentation from online newspapers like The Hindu, Hindustan Times and Times of India. The experimental results are based on the news categories and sub categories such as Entertainment: Bollywood 100% and Hollywood 90%, Sports: Cricket 90%, Football 90% and Hockey 78%, Matrimonial :Hindu 100% and Muslim 80%. In this paper we also compare the result of Hybrid algorithm (HMM, SVM and CART) with individual HMM and SVM Algorithm and conclude that Hybrid algorithm (HMM, SVM and CART) gave better result than that of what HMM and SVM individually gave.

DATA MINING KLASTERISASI PENJUALAN ALAT-ALAT BANGUNAN MENGGUNAKAN METODE K-MEANS (STUDI KASUS DI TOKO ADI BANGUNAN)

2018 ◽  
Vol 1 (2) ◽  
pp. 83-91
Author(s):  
M. Hasyim Siregar
Keyword(s):  
Data Mining ◽  
Cost Reduction ◽  
Building Materials ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Operational Cost ◽  
Use Of Data

In the world of business competition today, we are required to continually develop business to always survive in the competition. To achieve this there are a few things that can be done is to improve the quality of the product, adding the type of product and operational cost reduction company with how to use data analysis of the company. Data mining is a technology that automate the process to find interesting patterns and sensitive from the large data sets. This allows human understanding about finding patterns and scalability techniques. The store Adi Bangunan is a shop which is engaged in the sale of building materials and household who have such a system on supermarket namely buyers took own goods that will be purchased. Sales data, purchase goods or reimbursed some unexpected is not well ordered, so that the data is only function as archive for the store and cannot be used for the development of marketing strategy. In this research, data mining applied using the model of the process of K-Means that provides a standard process for the use of data mining in various areas used in the classification of because the results of this method can be easily understood and interpreted.

Benefits of Educational Data Mining

2020 ◽  
Vol 6 (1) ◽  
pp. 12-16
Author(s):  
Alisa Bilal Zorić
Keyword(s):  
Data Mining ◽  
Teaching And Learning ◽  
Educational Data Mining ◽  
Large Data ◽  
Research Area ◽  
Large Data Sets ◽  
Data Sets ◽  
Use Of Data ◽  
Learning Conditions ◽  
Methods And Techniques

We live in a world where we collect huge amounts of data, but if this data is not further analyzed, it remains only huge amounts of data. With new methods and techniques, we can use this data, analyze it and get a great advantage. The perfect method for this is data mining. Data mining is the process of extracting hidden and useful information and patterns from large data sets. Its application in various areas such as finance, telecommunications, healthcare, sales marketing, banking, etc. is already well known. In this paper, we want to introduce special use of data mining in education, called educational data mining. Educational Data Mining (EDM) is an interdisciplinary research area created as the application of data mining in the educational field. It uses different methods and techniques from machine learning, statistics, data mining and data analysis, to analyze data collected during teaching and learning. Educational Data Mining is the process of raw data transformation from large educational databases to useful and meaningful information which can be used for a better understanding of students and their learning conditions, improving teaching support as well as for decision making in educational systems.The goal of this paper is to introduce educational data mining and to present its application and benefits.

Effects of genotype and lactation number on health and reproductive problems in dairy cows

1997 ◽  
Vol 1997 ◽  
pp. 143-143
Author(s):  
B.L. Nielsen ◽  
R.F. Veerkamp ◽  
J.E. Pryce ◽  
G. Simm ◽  
J.D. Oldham
Keyword(s):  
Dairy Cows ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Variation Analysis ◽  
Genetic Line ◽  
Data Set ◽  
Health Events ◽  
Use Of Data ◽  
Low Incidence

High producing dairy cows have been found to be more susceptible to disease (Jones et al., 1994; Göhn et al., 1995) raising concerns about the welfare of the modern dairy cow. Genotype and number of lactations may affect various health problems differently, and their relative importance may vary. The categorical nature and low incidence of health events necessitates large data-sets, but the use of data collected across herds may introduce unwanted variation. Analysis of a comprehensive data-set from a single herd was carried out to investigate the effects of genetic line and lactation number on the incidence of various health and reproductive problems.

Data Mining Based Intelligent System for Voting Behavior Analysis

2013 ◽  
Vol 284-287 ◽  
pp. 3070-3073
Author(s):  
Duen Kai Chen
Keyword(s):  
Data Mining ◽  
Behavior Analysis ◽  
Voting Behavior ◽  
Intelligent System ◽  
Data Sets ◽  
Tree Model ◽  
Mining Technology ◽  
Identification Rate ◽  
Voter Identification ◽  
Election Studies

In this study, we report a voting behavior analysis intelligent system based on data mining technology. From previous literature, we have witnessed increasing number of studies applied information technology to facilitate voting behavior analysis. In this study, we built a likely voter identification model through the use of data mining technology, the classification algorithm used here constructs decision tree model to identify voters and non voters. This model is evaluated by its accuracy and number of attributes used to correctly identify likely voter. Our goal is to try to use just a small number of survey questions while maintaining the accuracy rates of other similar models. This model was built and tested on Taiwan’s Election and Democratization Study (TEDS) data sets. According to the experimental results, the proposed model can improve likely voter identification rate and this finding is consistent with previous studies based on American National Election Studies.

Data Mining: A Bagged Decision Tree Classifier Algorithm For Ids Intrusion Detection System Based Attacks Classification

2021 ◽  
pp. 1826-1839
Author(s):  
Sandeep Adhikari, Dr. Sunita Chaudhary
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Tree Classifier

The exponential growth in the use of computers over networks, as well as the proliferation of applications that operate on different platforms, has drawn attention to network security. This paradigm takes advantage of security flaws in all operating systems that are both technically difficult and costly to fix. As a result, intrusion is used as a key to worldwide a computer resource's credibility, availability, and confidentiality. The Intrusion Detection System (IDS) is critical in detecting network anomalies and attacks. In this paper, the data mining principle is combined with IDS to efficiently and quickly identify important, secret data of interest to the user. The proposed algorithm addresses four issues: data classification, high levels of human interaction, lack of labeled data, and the effectiveness of distributed denial of service attacks. We're also working on a decision tree classifier that has a variety of parameters. The previous algorithm classified IDS up to 90% of the time and was not appropriate for large data sets. Our proposed algorithm was designed to accurately classify large data sets. Aside from that, we quantify a few more decision tree classifier parameters.

Bibliomining for Library Decision-Making

2011 ◽  
pp. 341-345
Author(s):  
Scott Nicholson ◽  
Jeffrey Stanton
Keyword(s):  
Data Mining ◽  
Digital Libraries ◽  
Large Data ◽  
Data Sets ◽  
Data Mining Techniques ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
The People ◽  
The World ◽  
Use Of The Internet

Most people think of a library as the little brick building in the heart of their community or the big brick building in the center of a campus. These notions greatly oversimplify the world of libraries, however. Most large commercial organizations have dedicated in-house library operations, as do schools, non-governmental organizations, as well as local, state, and federal governments. With the increasing use of the Internet and the World Wide Web, digital libraries have burgeoned, and these serve a huge variety of different user audiences. With this expanded view of libraries, two key insights arise. First, libraries are typically embedded within larger institutions. Corporate libraries serve their corporations, academic libraries serve their universities, and public libraries serve taxpaying communities who elect overseeing representatives. Second, libraries play a pivotal role within their institutions as repositories and providers of information resources. In the provider role, libraries represent in microcosm the intellectual and learning activities of the people who comprise the institution. This fact provides the basis for the strategic importance of library data mining: By ascertaining what users are seeking, bibliomining can reveal insights that have meaning in the context of the library’s host institution. Use of data mining to examine library data might be aptly termed bibliomining. With widespread adoption of computerized catalogs and search facilities over the past quarter century, library and information scientists have often used bibliometric methods (e.g., the discovery of patterns in authorship and citation within a field) to explore patterns in bibliographic information. During the same period, various researchers have developed and tested data mining techniques—advanced statistical and visualization methods to locate non-trivial patterns in large data sets. Bibliomining refers to the use of these bibliometric and data mining techniques to explore the enormous quantities of data generated by the typical automated library.

Audio and Speech Processing for Data Mining

2011 ◽  
pp. 98-103 ◽  
Author(s):  
Zheng-Hua Tan
Keyword(s):  
Data Mining ◽  
Speech Processing ◽  
Large Data ◽  
Data Preprocessing ◽  
Multimedia Data ◽  
Data Sets ◽  
Data Types ◽  
Multimedia Data Mining ◽  
Customer Preferences ◽  
And Storage

The explosive increase in computing power, network bandwidth and storage capacity has largely facilitated the production, transmission and storage of multimedia data. Compared to alpha-numeric database, non-text media such as audio, image and video are different in that they are unstructured by nature, and although containing rich information, they are not quite as expressive from the viewpoint of a contemporary computer. As a consequence, an overwhelming amount of data is created and then left unstructured and inaccessible, boosting the desire for efficient content management of these data. This has become a driving force of multimedia research and development, and has lead to a new field termed multimedia data mining. While text mining is relatively mature, mining information from non-text media is still in its infancy, but holds much promise for the future. In general, data mining the process of applying analytical approaches to large data sets to discover implicit, previously unknown, and potentially useful information. This process often involves three steps: data preprocessing, data mining and postprocessing (Tan, Steinbach, & Kumar, 2005). The first step is to transform the raw data into a more suitable format for subsequent data mining. The second step conducts the actual mining while the last one is implemented to validate and interpret the mining results. Data preprocessing is a broad area and is the part in data mining where essential techniques are highly dependent on data types. Different from textual data, which is typically based on a written language, image, video and some audio are inherently non-linguistic. Speech as a spoken language lies in between and often provides valuable information about the subjects, topics and concepts of multimedia content (Lee & Chen, 2005). The language nature of speech makes information extraction from speech less complicated yet more precise and accurate than from image and video. This fact motivates content based speech analysis for multimedia data mining and retrieval where audio and speech processing is a key, enabling technology (Ohtsuki, Bessho, Matsuo, Matsunaga, & Kayashi, 2006). Progress in this area can impact numerous business and government applications (Gilbert, Moore, & Zweig, 2005). Examples are discovering patterns and generating alarms for intelligence organizations as well as for call centers, analyzing customer preferences, and searching through vast audio warehouses.

Visual Data Mining for Discovering Association Rules

2008 ◽  
pp. 2105-2120
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

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