Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
2014
Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the data mining tools which have been developed to support knowledge management process.Data mining has become an essential factor in various fields including business, education, health care, finance, scientific etc because of the large amount of the data. To analyse this vast amount of data and depict the fruitful conclusions and inferences, it needs specific data mining tools. This paper discusses the knowledge discovery process, data mining, various open source tools in the field of data mining from past to the present and explores the future trends.
Today Data Engineering assumes a crucial part in every parts of the human life. It is extremely vital to assemble information from diverse sources Lots of data mining tools are available in the market, but each tool has their own strengths and weaknesses. But Data mining means how to search relevant data from large database. And to solve this kind of broblem we use the concept of data mining It means by using various kind of data mining tools we easily find out the information from large data base.without wasting our time. In this paper we discuss various type of tools which help in data mining.there are certain measures which checked by organization before purchasing the mining tool .organisation only purchase that tool which meets the requirement of organization.because eevery organization has different requirements of mininig.this paper only give the description of various type of data mining tools. This paper examines the learning revelation process, information mining, differen...
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022
Data mining is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. It is well-known that data mining is a significant sub-field of knowledge management and that it is one of the most important information exploration phases in the database cycle. Over the ensuing decades, the practice of data mining will increase in market and learning companies. An introduction to the fundamentals of data mining is provided in this paper.
International journal of engineering research and technology, 2015
Data mining plays a vital role in the contemporary society and the corporate world as a whole. This paper reviews a number of different data mining tools including Environment for Knowledge Analysis (WEKA), Konstanz Information Miner (KNIME), GhostMiner, R Analytical Tool To Learn Easily (Rattle) and RapidMiner. More often than not, young researchers face the challenge of making choice of a data mining tool to carry out their research. An evaluation of the capabilities, attributes, as well as sources has also been done in this paper. The strengths and weakness of these tools has also been explored. It was established herein, that Waikato Environment for Knowledge Analysis (WEKA), Konstanz Information Miner (KNIME), R Analytical Tool To Learn Easily (Rattle) and RapidMiner are open source data mining tools and are provided under the GNU GPL licenses while GhostMiner is commercial.
2014
The primary social and economic value of modern societies is knowledge. For this reason, mastery of Information technology for structured or unstructured information recorded in Data warehouses such as the web is indispensable for the development of both the individuals and the society. Data mining and knowledge management (DMKM) has become essential for improving the competitiveness of businesses and increasing access to knowledge. DMKM still, however, comes up against major scientific and technological obstacles. Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant field in knowledge management. This paper explores the applications of data mining techniques which have been developed to support knowledge management process. Keywords— Data Mining; Data mining applications; Knowledge management.
International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, 2015
The enormous growth in the scale of data observed in recent years is a vital factor. It can be defined as high volume, velocity, and various data that require new high-performance processing. Addressing data is a challenging and time-demanding task requiring extensive computational infrastructure to ensure successful data processing and analysis. The presence of preprocessing data methods for data mining review in this paper. The definition, characteristics, and categorization of preprocessing data approaches are introduced. The connection between data and preprocessing throughout all methods and technologies is examined and covers a state-of-the-art review. Additionally, research issues focus on performance, data, and other issues in some families of data preprocessing methods and applications on new prominent data learning patterns. It provides information about knowledge discovery databases and significant issues associates with data mining techniques.
International Journal of Innovative Research in Computer Science & Technology, 2019
Data mining is the process of extracting hidden and useful patterns and information from data. Data mining is a new technology that helps businesses to predict future trends and behaviors, allowing them to make proactive, knowledge driven decisions. The aim of this paper is to show the process of data mining and how it can help decision makers to make better decisions. Practically, data mining is really useful for any organization which has huge amount of data. Data mining help regular databases to perform faster. They also help to increase the profit, because of the correct decisions made with the help of data mining. This paper shows the various steps performed during the process of data mining and how it can be used by various industries to get better answers from huge amount of data.
Knowledge Engineering Review, 2010
Up to now, many data mining and knowledge discovery methodologies and process models have been developed, with varying degrees of success. In this paper, we describe the most used (in industrial and academic projects) and cited (in scientific literature) data mining and knowledge discovery methodologies and process models, providing an overview of its evolution along data mining and knowledge discovery history and setting down the state of the art in this topic. For every approach, we have provided a brief description of the proposed knowledge discovery in databases (KDD) process, discussing about special features, outstanding advantages and disadvantages of every approach. Apart from that, a global comparative of all presented data mining approaches is provided, focusing on the different steps and tasks in which every approach interprets the whole KDD process. As a result of the comparison, we propose a new data mining and knowledge discovery process named refined data mining process for developing any kind of data mining and knowledge discovery project. The refined data mining process is built on specific steps taken from analyzed approaches.
This Paper gives an introduction of Data Mining System and development of different data mining systems. How many data mining tools or systems really exist? What are different platform under which these tools or systems has been designed? What are different areas for which these data mining system has been devised or developed. After doing study and research on data mining system. we have to decide which platform is best for the development of Data Mining System and to understand the importance of data mining system and platform on which it has been developed. As we know what is importance of data mining system for big and large organization, when the company is working in different parts of country and producing large amount of data then it very important to analyze that data which account for productivity and cost benefits from that data. Now it is important to understand that which part of the organization is giving benefit and where the organization is losing. So to take any dec...
2009
The available data mining tools require considerable knowledge of the user to perform the entire process of knowledge discovery in databases (KDD). Teaching how to prepare the data, how to choose the appropriate data mining task and how to analyze the generated rules are beyond the objectives of the current tools. This paper proposes several guides to abstract and apply the domain knowledge concerning the data mining process and describes a tool named Kira which was developed based on these guides. The main purpose of the tool is to instruct users to conduct, more easily, the KDD process.
Nowadays various fields of industries and studies require data mining tools to extract knowledge from variety of databases. Developing such data mining tool is nontrivial task, due to selections required from variety of available algorithms, professionally. In this paper Online Interactive Incremental Data Mining tool (OIIDM) is presented. This tool provides variety of data mining tasks like clustering and incremental Clustering, classification, association mining. These tasks are achieved through interacting with user to provide satisfaction of performed task. OIIDM help user to get appropriate data mining algorithm among the available algorithms by performing analysis of algorithm based on input data by Considering Algorithmic parameter. This tool also support to the incremental approach of data mining to user as incremental data is one of the issues in data mining. © 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of scientific committee of 2nd International Symposium on Big Data and Cloud Computing (ISBCC'15).
International Journal of Advanced Research in Computer Science and Software Engineering
Today practically everybody approaches colossal measure of information. A few across the board associations have their own particular extensive information storehouses, information stockrooms, which are as yet extending with questions over information and the requirement for extraction of most useful information design and refined learning. , Data can now be put away in a wide range of sorts of databases and data archives, other than being accessible on the Internet or in printed frame. With such measure of information, there is a requirement for effective systems for better translation of this information. Analysts needs these sort of hardware for examination their information. In this paper, we are talked about different accessible information mining devices and look at their utilities. There are such a variety of instruments are there like WEKA, orange, Rapid Miner, Tanagra etc. Information mining and it's applications can be seen as one of the rising and promising innovative improvements that give effective intends to get to different sorts of information and data accessible around the world. Not just this, these applications likewise helps in basic leadership. A superior comprehension of these applications helps in asking decision among all accessible application and devices. The paper gives the complete and hypothetical investigation of six open source information mining apparatuses. The review portrays the specialized particular, components, and specialization for each chose device alongside its applications. By utilizing the review the decision and determination of apparatuses can be made simple.
This paper addresses the discussion of impact of data mining in today's fast growing world. Data mining means the extraction of hidden predictive information from large databases. It is an interactive information discovery process that includes data acquisition, data integration, data exploration, model building, and model validation. This paper provides an overview of data mining process, some well known techniques, software and few application areas.
International Journal of Academic Research in Business and Social Sciences, 2022
Nowadays, some of the intersting roles of human life are data, information, and knowledge. Analyzing and modelling of big data have been required by data massive storehouses together with the rapid technologies growth to predict and analyze the future trends of information. Methodologies and techniques, which are employed into diverse information systems scope, are needed for detection of knowing in the databases. The technology which extracts advantageous information to discover knowledge is called Data Mining. Data mining, it has been defined as discovery of knowledge in data (KDD), it is the disclosure of modalities procedures and other valuable information from considerable sets of data. It has been a tremendous progress in machine learning, artificial agent systems, and decision-making in the expert systems. In the last decades, most of the techniques and applications has been surveyed via the researchers. Those techniques and applications are utilized in distinct areas in daily life like industrialization, education, engineering, commerce and business. Searching last years researches about the review of the most techniques and trends of data mining in multiple areas was the method which is followed in this paper. It has discovered in the learning field as diffusing data mining for educating activities, improvement quality of tasks into manufacturing field, text mining as a technique into research databases and so on. This study collectes a summary of information about the basci concept of Data Mining and its technques which other researchers may need to start their studies in Data Mining field.
International Journal of Engineering and Technology, 2017
The knowledge discovery process is done using the data mining techniques by transforming the raw data from various sources into meaningful patterns and further interpreting those into useful information. Various data mining algorithms like K-Means or a Priori may be used for the purpose of extraction of meaningful patterns or trends in the given data. This review paper contains various data mining techniques in a comprehendible way.
International Journal of Distributed and Parallel systems, 2010
In the Information Technology era information plays vital role in every sphere of the human life. It is very important to gather data from different data sources, store and maintain the data, generate information, generate knowledge and disseminate data, information and knowledge to every stakeholder. Due to vast use of computers and electronics devices and tremendous growth in computing power and storage capacity, there is explosive growth in data collection. The storing of the data in data warehouse enables entire enterprise to access a reliable current database. To analyze this vast amount of data and drawing fruitful conclusions and inferences it needs the special tools called data mining tools. This paper gives overview of the data mining systems and some of its applications.
International Journal of Engineering Research and, 2017
Today the magnificient growth of technology and adoption of the several application renaissance in the information technology sector and the related fields.Due to this striking advancement ,collecting and warehousing the data in necessity.This overall leads to the concept of data mining,which can be viewed as one of the emerging and promising technology development. Data mining is explanation and analysis of large quantities of data in order to extract implicit, previously unknown and potentially meaningful patterns by using some tools and techniques. This paper presents the comprehensive and theoretical analysis of five open source data mining tools-Rapidminer, R, Knime, Orange, Weka. The study provides the pros and cons Ziped with the technical specifications features and specialization of each tool.By this complete and hypothetical study, the best slelection of the tool can be made easy.
Data mining, the extraction of hidden predictive information from large databases, invented as a powerful new technology with great potential to help companies and organizations to focus on the most important information in their data warehouses. Data mining uses machine learning, statistical and visualization techniques to discovery and present knowledge in a form which is easily comprehensible to humans. Various popular data mining tools and techniques are available today for supporting large amount of applications. Data mining tools predict future trends and behaviors, allowing it’s users to make proactive, knowledge-driven decisions. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. With these large amount of features and applications there are some challenging issues also which are not exclusive and are not ordered in any way. This paper presents an overview of the data mining technique, Some of its vital applications and issues needs to be addressed.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
The Powerful software tools and techniques required for the development of data mining applications. With the rapid development of technologies and business interest in using electronics and latest technologies plays important role in improvement of data mining field. Data mining access the meaningful and efficient information available in worldwide which is helps in decision making. This paper described the (a) various tools and techniques used by data mining applications. (b) compared features and limitations both in Proprietary and open sources data mining tools. (c) technical analysis of proprietary and open source data mining tools. On the basis of well-designed User interface, short time analysis, statistical and mathematical analysis user can select the best tool as per their requirements. Analysis of these tools makes easy to select appropriate tool.
International Journal of Research in Advent Technology
The paper studies distinctive parts of information mining research. Information mining is useful in getting information from expansive spaces of databases, information stockrooms and information bazaars. Extraordinary and current zones of information mining moreover talked about. Issues and difficulties of information mining alongside different open source instruments are tended to also. Information mining is an imperative what's more, developing examination territory and utilized by the researcher to analysts and PC researchers too. Keywords-information mining, learning revelation in databases, regions and instruments in information mining, difficulties of information mining.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.