A Novel Method for Formulating the Business Objectives of Data Mining Projects

2015 ◽  
pp. 74-101
Keyword(s):  
Data Mining ◽  
Business Objectives ◽  
Novel Method ◽  
Mining Projects

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.

Proposal of Analytical Model for Business Problems Solving in Big Data Environment

Web Services ◽  
2019 ◽  
pp. 618-638
Author(s):  
Goran Klepac ◽  
Kristi L. Berg
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Predictive Models ◽  
Analytical Approach ◽  
Fraud Detection ◽  
Analytical Techniques ◽  
Data Sources ◽  
Business Decisions ◽  
Mining Projects ◽  
Structured Approach

This chapter proposes a new analytical approach that consolidates the traditional analytical approach for solving problems such as churn detection, fraud detection, building predictive models, segmentation modeling with data sources, and analytical techniques from the big data area. Presented are solutions offering a structured approach for the integration of different concepts into one, which helps analysts as well as managers to use potentials from different areas in a systematic way. By using this concept, companies have the opportunity to introduce big data potential in everyday data mining projects. As is visible from the chapter, neglecting big data potentials results often with incomplete analytical results, which imply incomplete information for business decisions and can imply bad business decisions. The chapter also provides suggestions on how to recognize useful data sources from the big data area and how to analyze them along with traditional data sources for achieving more qualitative information for business decisions.

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.

scholarly journals A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
José García ◽  
Christopher Pope ◽  
Francisco Altimiras
Keyword(s):  
Data Mining ◽  
Early Detection ◽  
Mobile Application ◽  
Image Data ◽  
Lobesia Botrana ◽  
Segmentation Algorithm ◽  
Gray Scale ◽  
Image Capture ◽  
Major Pest ◽  
Novel Method

Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud.

scholarly journals A Novel Method for Preserving Privacy in Big-Data Mining

2014 ◽  
Vol 103 (16) ◽  
pp. 21-25
Author(s):  
Nasrin IrshadHussain ◽  
Bharadwaj Choudhury ◽  
Sandip Rakshit
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Big Data Mining ◽  
Novel Method

scholarly journals Making Use of Functional Dependencies Based on Data to Find Better Classification Trees

2021 ◽  
Vol 15 ◽  
pp. 1475-1485
Author(s):  
Hyontai Sug
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Trees ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Functional Dependencies ◽  
Chi Square ◽  
Chi Square Test ◽  
Novel Method ◽  
Categorical Attributes

For the classification task of machine learning algorithms independency between conditional attributes is a precondition for success of data mining. On the other hand, decision trees are one of the mostly used machine learning algorithms because of their good understandability. So, because dependency between conditional attributes can cause more complex trees, supplying conditional attributes independent each other is very important, the requirement of conditional attributes for decision trees as well as other machine learning algorithms is that they are independent each other and dependent on decisional attributes only. Statistical method to check independence between attributes is Chi-square test, but the test can be effective for categorical attributes only. So, the applicability of Chi-square test is limited, because most datasets for data mining have mixed attributes of categorical and numerical. In order to overcome the problem, and as a way to test dependency between conditional attributes, a novel method based on functional dependency based on data that can be applied to any datasets irrespective of data type of attributes is suggested. After removing highly dependent attributes between conditional attributes, we can generate better decision trees. Experiments were performed to show that the method is effective, and the experiments showed very good results.

scholarly journals A novel method of symbolic representation in diving data mining: A case study of highways in China

2018 ◽  
pp. e5115
Author(s):  
Chuan Sun ◽  
Wei Liu ◽  
Duanfeng Chu ◽  
Wushuang Li ◽  
Zhenji Lu ◽  
...  
Keyword(s):  
Data Mining ◽  
Symbolic Representation ◽  
Novel Method
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