scholarly journals A Novel Method Improvement of Rapid Miner for the Data Mining Applications

2019 ◽  
Vol 8 (9S3) ◽  
pp. 457-460
Keyword(s):  
Data Mining ◽  
Information Science ◽  
Information Mining ◽  
Learning Content ◽  
Business Applications ◽  
Content Mining ◽  
Perception Model ◽  
Novel Method ◽  
Research Instruction ◽  
Method Improvement

Information mining is a well known mechanical advancement that changes over heaps of information into useful learning, which can help the information owners or clients settle on educated decisions and take sharp developments for their own favorable position. Quick Miner is an information science programming stage created by the organization of a similar name that gives a coordinated situation to information readiness, AI, profound learning, content mining, and prescient investigation. It is utilized for business and business applications just as for research, instruction, preparing, quick prototyping, and application improvement and supports all means of the AI procedure including information readiness, results perception, model approval and advancement.

scholarly journals A headway methodology for online web-based social network against cybercriminal mining

2018 ◽  
Vol 7 (1.9) ◽  
pp. 254
Author(s):  
R.Josepine Leela ◽  
R.V. Jayasree ◽  
M. Krishna Raj ◽  
K. Gopinath
Keyword(s):  
Data Mining ◽  
Social Networking ◽  
Data Recovery ◽  
Information Mining ◽  
Web Based ◽  
Content Mining ◽  
Recovery Design ◽  
Content Information ◽  
Online Web ◽  
Predetermined Number

Data mining is the way toward gathering information from various setting and condenses them into helpful data. Information mining can be utilized to decide the connection between inside elements and outside elements .It permits the clients to investigate, order and decides the connections deduced in them. Content mining as a rule alluded to as content information mining can be utilized be utilized to concentrate data from content. Content mining can be utilized as a part of data recovery, design acknowledgment and information mining systems. The presentation of online networking and interpersonal organizations has changed the open doors accessible for us as well as we should be careful about the dangers. Late explores demonstrate that the quantity of wrongdoings are expanding through online web-based social networking and they may bring about enormous misfortune to associations. Existing digital advancements are not viable to secure organizations. Existing mining techniques focus on dictionaries in which they can distinguish just a predetermined number of relations. Here a hereditary calculation approach is presented in which inert ideas can be removed. Hereditary Calculation is a straight pursuit which requires just little data from vast hunt zone.. At that point these ideas are subjected to separate the semantics which construes the comparing connections. Hereditary calculation gives a superior arrangement in which exactness and time effectiveness can be moved forward. The principle commitment of the paper demonstrates that they distinguish the relating cybercriminal systems.

scholarly journals A Novel Method of Rapid Miner for the Data Mining Applications

2019 ◽  
Vol 8 (9S3) ◽  
pp. 452-456
Keyword(s):  
Data Mining ◽  
Neural Systems ◽  
Information Mining ◽  
Multidimensional Database ◽  
The Past ◽  
Work Area ◽  
Novel Method ◽  
Points Of View

Information mining is a renowned mechanical development that changes over heaps of information into useful learning, which can help the information owners/clients settle on educated decisions and take shrewd developments for their own bit of leeway. In remarkable terms, actualities digging searches for shrouded designs among boundless arrangements of information that may help to catch, expect, and direct fate lead. A more prominent specialized clarification: data mining is the arrangement of procedures used in perusing information from various measurements and points of view, finding once in the past obscure shrouded styles, ordering and gathering the data and condensing the analyzed connections. The components of realities mining comprise of extraction, change, and stacking of information onto the insights stockroom gadget, dealing with data in a multidimensional database contraption, offering get right of section to business undertaking examiners and it specialists, perusing the records with the guide of apparatuses, and providing the information in a valuable format, comprehensive of a chart or work area. That is finished with the guide of distinguishing seeking utilizing classes, groups, affiliations, and successive examples by utilizing the utilization of measurable examination, device inclining and neural systems.

A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 36-53
Author(s):  
Evis Trandafili ◽  
Marenglen Biba
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Networks ◽  
Large Scale ◽  
Viral Marketing ◽  
Social Network Mining ◽  
Business Applications ◽  
Mining Community ◽  
Recent Developments ◽  
Analysis Of Social Networks

Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution of such networks has posed outstanding challenges for the learning and mining community, and on the other has opened the possibility for very powerful business applications. However, little understanding exists regarding these business applications and the potential of social network mining to boost marketing. This paper presents a review of the most important state-of-the-art approaches in the machine learning and data mining community regarding analysis of social networks and their business applications. The authors review the problems related to social networks and describe the recent developments in the area discussing important achievements in the analysis of social networks and outlining future work. The focus of the review in not only on the technical aspects of the learning and mining approaches applied to social networks but also on the business potentials of such methods.

scholarly journals Privacy Preserving In Data Mining with No Data Loss with a Combinational Scheme

2019 ◽  
Vol 8 (4S3) ◽  
pp. 457-459
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Data Reconstruction ◽  
Sensitive Information ◽  
Data Loss ◽  
Data Mining Technique ◽  
Information Mining ◽  
Mining Technique

Large amounts of data collected by many organizations under-goes data mining for various purposes like analysis and prediction. During data mining tasks, the sensitive information may be losing its privacy. Hence, Privacyprotection or preservation is becomes major issue for the organizations. Publishing data or sharing information for mining with Privacypreservation is possible through Privacypreserve data mining technique (PPDM). Existing techniques are not able to withstand for some attacks and some suffers with data misfortune. In our paper we conventional an effective and combinational approach for security safeguarding in information mining. Our approach with can withstand from different kinds of assaults and limits data misfortune and increases data re-usability with data reconstruction capability

scholarly journals Student Future Prediction System Under Filtering Mechanism

2019 ◽  
Vol 8 (9S2) ◽  
pp. 761-765
Keyword(s):  
Information Science ◽  
Practice Patterns ◽  
Information Source ◽  
Time Estimation ◽  
Contextual Analysis ◽  
Prediction System ◽  
Information Mining ◽  
Profile Information ◽  
Future Prediction ◽  
Day By Day

Information is progressively being utilized to improve regular day to day existence less demanding and. Applications, for example, holding up time estimation, traffic expectation, and stopping look are genuine instances of how information from various sources can be utilized to encourage our day by day life.In this period of computerization, instruction has additionally patched up itself and isn't constrained to old address strategy. The ordinary mission is on to discover better approaches to make it increasingly successful and effective for understudies. These days, loads of information is gathered in instructive databases, yet it remains unutilized. So as to get required advantages from such major information, amazing assets are required. Information mining is a developing integral asset for investigation and forecast. In this investigation, we consider an under-used information source: college ID cards. Such cards are utilized on numerous grounds to buy nourishment, enable access to various territories, and even gauge participation in classes. In this article, we use information from our college to investigate use of the college wellness focus and fabricate an indicator for future visit volume. The work makes a few commitments: it exhibits the extravagance of the information source, demonstrates how the information can be utilized to improve understudy administrations, finds fascinating patterns and conduct, and fills in as a contextual analysis outlining the whole information science process. One objective of this article is to show the information science process. As far as information accumulation, two arrangements of information—timestamp information from card swipes at the Student Recreation Centre (SRC) and client profile information—were gathered from our Management of University. The gathered information was cleaned and further prepared. At that point, exploratory information investigation was performed to find fascinating examples. General understudy practice patterns were found from the timestamp dataset. These incorporate yearly/month to month/every day frequencies of understudy visits to the SRC and pinnacle hours amid multi day

Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset

2022 ◽  
Vol 13 (1) ◽  
pp. 1-17
Author(s):  
Ankit Kumar ◽  
Abhishek Kumar ◽  
Ali Kashif Bashir ◽  
Mamoon Rashid ◽  
V. D. Ambeth Kumar ◽  
...  
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Outlier Detection ◽  
Credit Card ◽  
High Dimensional ◽  
Work Efficiency ◽  
Average Value ◽  
Novel Method ◽  
Detection Of Outliers ◽  
Better Than

Detection of outliers or anomalies is one of the vital issues in pattern-driven data mining. Outlier detection detects the inconsistent behavior of individual objects. It is an important sector in the data mining field with several different applications such as detecting credit card fraud, hacking discovery and discovering criminal activities. It is necessary to develop tools used to uncover the critical information established in the extensive data. This paper investigated a novel method for detecting cluster outliers in a multidimensional dataset, capable of identifying the clusters and outliers for datasets containing noise. The proposed method can detect the groups and outliers left by the clustering process, like instant irregular sets of clusters (C) and outliers (O), to boost the results. The results obtained after applying the algorithm to the dataset improved in terms of several parameters. For the comparative analysis, the accurate average value and the recall value parameters are computed. The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm.

First Look on Web Mining Techniques to Improve Business Intelligence of E-Commerce Applications

2017 ◽  
pp. 298-314 ◽  
Author(s):  
G. Sreedhar ◽  
A. Anandaraja Chari
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Web Content ◽  
Web Data ◽  
Web Data Mining ◽  
Useful Knowledge ◽  
Web Usage ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining

Web Data Mining is the application of data mining techniques to extract useful knowledge from web data like contents of web, hyperlinks of documents and web usage logs. There is also a strong requirement of techniques to help in business decision in e-commerce. Web Data Mining can be broadly divided into three categories: Web content mining, Web structure mining and Web usage mining. Web content data are content availed to users to satisfy their required information. Web structure data represents linkage and relationship of web pages to others. Web usage data involves log data collected by web server and application server which is the main source of data. The growth of WWW and technologies has made business functions to be executed fast and easier. As large amount of transactions are performed through e-commerce sites and the huge amount of data is stored, valuable knowledge can be obtained by applying the Web Mining techniques.

Analytical Competition for Managing Customer Relations

2011 ◽  
pp. 25-30 ◽  
Author(s):  
Dan Zhu
Keyword(s):  
Data Mining ◽  
World Wide Web ◽  
Text Mining ◽  
World Wide ◽  
Customer Relationship ◽  
Service Access ◽  
Web Content ◽  
The World ◽  
Content Mining ◽  
Automated Tools

With the advent of technology, information is available in abundance on the World Wide Web. In order to have appropriate and useful information users must increasingly use techniques and automated tools to search, extract, filter, analyze and evaluate desired information and resources. Data mining can be defined as the extraction of implicit, previously unknown, and potentially useful information from large databases. On the other hand, text mining is the process of extracting the information from an unstructured text. A standard text mining approach will involve categorization of text, text clustering, and extraction of concepts, granular taxonomies production, sentiment analysis, document summarization, and modeling (Fan et al, 2006). Furthermore, Web mining is the discovery and analysis of useful information using the World Wide Web (Berry, 2002; Mobasher, 2007). This broad definition encompasses “web content mining,” the automated search for resources and retrieval of information from millions of websites and online databases, as well as “web usage mining,” the discovery and analysis of users’ website navigation and online service access patterns. Companies are investing significant amounts of time and money on creating, developing, and enhancing individualized customer relationship, a process called customer relationship management or CRM. Based on a report by the Aberdeen Group, worldwide CRM spending reached close to $20 billion by 2006. Today, to improve the customer relationship, most companies collect and refine massive amounts of data available through the customers. To increase the value of current information resources, data mining techniques can be rapidly implemented on existing software and hardware platforms, and integrated with new products and systems (Wang et al., 2008). If implemented on high-performance client/server or parallel processing computers, data mining tools can analyze enormous databases to answer customer-centric questions such as, “Which clients have the highest likelihood of responding to my next promotional mailing, and why.” This paper provides a basic introduction to data mining and other related technologies and their applications in CRM.

Improved Privacy

2011 ◽  
pp. 295-316
Author(s):  
K. Abumani ◽  
R. Nedunchezhian
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Empirical Analysis ◽  
Time Complexity ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Sensitive Information ◽  
Data Mining Techniques ◽  
Novel Method ◽  
Mining Tools

Data mining techniques have been widely used for extracting non-trivial information from massive amounts of data. They help in strategic decision-making as well as many more applications. However, data mining also has a few demerits apart from its usefulness. Sensitive information contained in the database may be brought out by the data mining tools. Different approaches are being utilized to hide the sensitive information. The proposed work in this article applies a novel method to access the generating transactions with minimum effort from the transactional database. It helps in reducing the time complexity of any hiding algorithm. The theoretical and empirical analysis of the algorithm shows that hiding of data using this proposed work performs association rule hiding quicker than other algorithms.

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