scholarly journals PERFORMANCE VALIDATION OF PRIOR QUANTIZATION TECHNIQUES IN OUTLIERS CLASSIFICATION USING WDBC DATASET

2020 ◽  
Vol 5 (4) ◽  
pp. 48-56
Author(s):  
D. Rajakumari
Keyword(s):  
Data Mining ◽  
Decision Making Process ◽  
Data Sets ◽  
Distance Metrics ◽  
Medical Practitioners ◽  
Useful Knowledge ◽  
Large Size ◽  
Performance Validation ◽  
Sequential Scanning ◽  
Monotonic Classification

Data mining is the process of analyzing enormous data and summarizing it into the useful knowledge discovery and the task of data mining approaches is growing quickly, particularly classification techniques very efficient, way to classifying the data, which is important in the decision-making process for medical practitioners. This study presents the quantization and validation (OQV) techniques for fast outlier detection in large size WDBC data sets. The distance metrics utilization makes the algorithm as the linear one for various objects and assures the sequential scanning. The inclusion of direct quantization technique and the cluster explicit discovery assures the simplicity and the economical. The comparative analysis of proposed OQV techniques with the triangular boundary-based classification and the Weighing-based Feature Selection and Monotonic Classification (WFSMC) regarding the accuracy, precision, recall and the number of attributes assures an effectiveness of OQV for large size datasets.

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.

Visual Data Mining for Discovering Association Rules

2011 ◽  
pp. 209-226
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.

Video Data Mining

2011 ◽  
pp. 1185-1189 ◽  
Author(s):  
Jung Hwan Oh ◽  
Jeong Kyu Lee ◽  
Sae Hwang
Keyword(s):  
Data Mining ◽  
Research Area ◽  
Multimedia Databases ◽  
Video Data ◽  
Multimedia Data ◽  
Data Sets ◽  
Data Set ◽  
Useful Knowledge ◽  
Active Research ◽  
Diverse Data

Data mining, which is defined as the process of extracting previously unknown knowledge and detecting interesting patterns from a massive set of data, has been an active research area. As a result, several commercial products and research prototypes are available nowadays. However, most of these studies have focused on corporate data — typically in an alpha-numeric database, and relatively less work has been pursued for the mining of multimedia data (Zaïane, Han, & Zhu, 2000). Digital multimedia differs from previous forms of combined media in that the bits representing texts, images, audios, and videos can be treated as data by computer programs (Simoff, Djeraba, & Zaïane, 2002). One facet of these diverse data in terms of underlying models and formats is that they are synchronized and integrated hence, can be treated as integrated data records. The collection of such integral data records constitutes a multimedia data set. The challenge of extracting meaningful patterns from such data sets has lead to research and development in the area of multimedia data mining. This is a challenging field due to the non-structured nature of multimedia data. Such ubiquitous data is required in many applications such as financial, medical, advertising and Command, Control, Communications and Intelligence (C3I) (Thuraisingham, Clifton, Maurer, & Ceruti, 2001). Multimedia databases are widespread and multimedia data sets are extremely large. There are tools for managing and searching within such collections, but the need for tools to extract hidden and useful knowledge embedded within multimedia data is becoming critical for many decision-making applications.

scholarly journals Research of Production and Growth of Coriander in Various Seasons using K-Means Algorithm

2019 ◽  
Vol 8 (12S) ◽  
pp. 518-521
Keyword(s):  
Data Mining ◽  
Meteorological Data ◽  
Weather Condition ◽  
Large Data ◽  
Vital Role ◽  
Data Sets ◽  
Useful Knowledge ◽  
Productivity Data ◽  
The People ◽  
Important Field

The main employment and resource of our country is agriculture. In the upcoming days agriculture is going to be one of the important field .Agriculture plays a vital role in economical development of india. Half of the Indian population is mainly depended on agriculture. It is the source of living it is important in everyday life. Comparing to previous years Now-aday's Agriculture is in poor condition. The most important reasons for this is there is no proper guidance for the farmers.Outstanding to these problems, farming affects the yield of Coriander and lack of knowledge about the Coriander cultivation methodologies. And also season to cultivate the coriander and choosing which soil is the best to cultivate the particular Coriander based on the weather condition and also when to harvest the Coriander for the best yield. If the farmer is aware about the Coriander cultivation methodologies and harvesting it will more helpful for the people in the real world and also to increase the Coriander productivity. Data mining is the process of finding new template from large data sets, this technology which is in use in inferring useful knowledge that can be put to use from a vast amount of data. Climate is one of the meteorological data that is well-to-do by important knowledge. This paper presents a brief comparative study of various different techniques used for yield of coriander. The data mining techniques that are in use for the coriander yield estimation are K-Means.

Creating Competitive Advantage by Using Data Mining Technique as an Innovative Method for Decision Making Process in Business

2013 ◽  
pp. 205-213
Author(s):  
Mert Bal ◽  
Yasemin Bal ◽  
Ayse Demirhan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Competitive Advantage ◽  
Decision Making Process ◽  
Success Factor ◽  
Data Sets ◽  
Data Mining Technique ◽  
Critical Success ◽  
Using Data

Competitive advantage is at the heart of a firm’s performance in today’s challenging and rapidly changing environment. One of the central bases for achieving competitive advantage is the organizational capability to create new knowledge and transfer it across various levels of the organization. Traditional methods of data analysis, based mainly on human dealing directly with the data, simply do not scale to handle with large data sets. This explosive growth in data and databases has generated an urgent need for new techniques and tools that can intelligently and automatically transform the processed data into useful information and knowledge. Consequently, data mining has become a research area with increasing importance. Organizations of all sizes have started to develop and deploy data mining technologies to leverage data resources to enhance their decision making capabilities. Business information received from data analysis and data mining is a critical success factor for companies wishing to maximize competitive advantage. In this study, the importance of gaining knowledge for organizations in today’s competitive environment are discussed and data mining method in decision making process is analyzed as an innovative technique for organizations.

scholarly journals Application of Data Mining Method Using Association Rules Apriori To Shopping Cart Analysis On Sale Transactions (Case Study Alfamidi Burnt Stone)

2020 ◽  
Vol 2 (2) ◽  
pp. 222-226
Author(s):  
Vanessa Siregar ◽  
Paska Marto Hasugian
Keyword(s):  
Data Mining ◽  
Knowledge Discovery In Databases ◽  
Data Sets ◽  
Cart Analysis ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Large Size ◽  
Using Data ◽  
Mining Algorithms

Also Often data mining is called knowledge discovery in databases (KDD), ie activities include the collection, historical use of data to find regularities, patterns or relationships in data sets with a large size. The company may be interested to know if some groups consistently goods items purchased together. This study analyzes the transaction of data information retrieval from the sale of skin care and hair care using data mining algorithms priori Alfamidi Burnt Stones with the highest support value is 8% and the highest value is 5% confidance

Video Data Mining

2008 ◽  
pp. 1631-1637
Author(s):  
Jung Hwan Oh ◽  
Jeong Kyu Lee ◽  
Sae Hwang
Keyword(s):  
Data Mining ◽  
Research Area ◽  
Multimedia Databases ◽  
Video Data ◽  
Multimedia Data ◽  
Data Sets ◽  
Data Set ◽  
Useful Knowledge ◽  
Active Research ◽  
Diverse Data

Data mining, which is defined as the process of extracting previously unknown knowledge and detecting interesting patterns from a massive set of data, has been an active research area. As a result, several commercial products and research prototypes are available nowadays. However, most of these studies have focused on corporate data — typically in an alpha-numeric database, and relatively less work has been pursued for the mining of multimedia data (Zaïane, Han, & Zhu, 2000). Digital multimedia differs from previous forms of combined media in that the bits representing texts, images, audios, and videos can be treated as data by computer programs (Simoff, Djeraba, & Zaïane, 2002). One facet of these diverse data in terms of underlying models and formats is that they are synchronized and integrated hence, can be treated as integrated data records. The collection of such integral data records constitutes a multimedia data set. The challenge of extracting meaningful patterns from such data sets has lead to research and development in the area of multimedia data mining. This is a challenging field due to the non-structured nature of multimedia data. Such ubiquitous data is required in many applications such as financial, medical, advertising and Command, Control, Communications and Intelligence (C3I) (Thuraisingham, Clifton, Maurer, & Ceruti, 2001). Multimedia databases are widespread and multimedia data sets are extremely large. There are tools for managing and searching within such collections, but the need for tools to extract hidden and useful knowledge embedded within multimedia data is becoming critical for many decision-making applications.

Video Data Mining

2011 ◽  
pp. 2042-2047
Author(s):  
JungHwan Oh
Keyword(s):  
Data Mining ◽  
Research Area ◽  
Multimedia Databases ◽  
Video Data ◽  
Multimedia Data ◽  
Data Sets ◽  
Data Set ◽  
Useful Knowledge ◽  
Active Research ◽  
Diverse Data

Data mining, which is defined as the process of extracting previously unknown knowledge and detecting interesting patterns from a massive set of data, has been an active research area. As a result, several commercial products and research prototypes are available nowadays. However, most of these studies have focused on corporate data — typically in an alpha-numeric database, and relatively less work has been pursued for the mining of multimedia data (Zaïane, Han, & Zhu, 2000). Digital multimedia differs from previous forms of combined media in that the bits representing texts, images, audios, and videos can be treated as data by computer programs (Simoff, Djeraba, & Zaïane, 2002). One facet of these diverse data in terms of underlying models and formats is that they are synchronized and integrated hence, can be treated as integrated data records. The collection of such integral data records constitutes a multimedia data set. The challenge of extracting meaningful patterns from such data sets has lead to research and development in the area of multimedia data mining. This is a challenging field due to the non-structured nature of multimedia data. Such ubiquitous data is required in many applications such as financial, medical, advertising and Command, Control, Communications and Intelligence (C3I) (Thuraisingham, Clifton, Maurer, & Ceruti, 2001). Multimedia databases are widespread and multimedia data sets are extremely large. There are tools for managing and searching within such collections, but the need for tools to extract hidden and useful knowledge embedded within multimedia data is becoming critical for many decision-making applications.

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