Biological Data Mining

2008 ◽  
pp. 1696-1705
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Data Mining ◽  
New Technologies ◽  
Noisy Data ◽  
Large Data ◽  
Biological Data ◽  
Customer Relationship ◽  
Mass Storage ◽  
Stock Management ◽  
Management Fraud ◽  
Storage Media

At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).

Biological Data Mining

2005 ◽  
pp. 35-41 ◽  
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Data Mining ◽  
New Technologies ◽  
Noisy Data ◽  
Large Data ◽  
Biological Data ◽  
Customer Relationship ◽  
Mass Storage ◽  
Stock Management ◽  
Management Fraud ◽  
Storage Media

At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).

Enterprise Information System and Data Mining

2012 ◽  
pp. 228-235
Author(s):  
Kenneth D. Lawrence ◽  
Dinesh R. Pai ◽  
Ronald Klimberg ◽  
Sheila M. Lawrence
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Information Systems ◽  
Business Intelligence ◽  
Large Data ◽  
Customer Relationship ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Competitive Edge ◽  
Organizational Functions

The advent of information technology and the consequent proliferation of information systems have lead to generation of vast amounts of data, both within the organization and across its supply chain. Enterprise information systems (EIS) have added to organizational complexity, and at the same time, created opportunities for enhancing its competitive advantage by utilizing this data for business intelligence purposes. Various data mining tools have been used to gain a competitive edge through these large data bases. In this paper, the authors discuss EIS-aided business intelligence and data mining as applicable to organizational functions, such as supply chain management (SCM), marketing, and customer relationship management (CRM) in the context of EIS.

Enterprise Information System and Data Mining

2010 ◽  
Vol 1 (3) ◽  
pp. 34-41 ◽  
Author(s):  
Kenneth D. Lawrence ◽  
Dinesh R. Pai ◽  
Ronald Klimberg ◽  
Sheila M. Lawrence
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Information Systems ◽  
Business Intelligence ◽  
Large Data ◽  
Customer Relationship ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
Competitive Edge ◽  
Organizational Functions

The advent of information technology and the consequent proliferation of information systems have lead to generation of vast amounts of data, both within the organization and across its supply chain. Enterprise information systems (EIS) have added to organizational complexity, and at the same time, created opportunities for enhancing its competitive advantage by utilizing this data for business intelligence purposes. Various data mining tools have been used to gain a competitive edge through these large data bases. In this paper, the authors discuss EIS-aided business intelligence and data mining as applicable to organizational functions, such as supply chain management (SCM), marketing, and customer relationship management (CRM) in the context of EIS.

Plant functional genomics: opportunities in microarray databases and data mining

2004 ◽  
Vol 31 (4) ◽  
pp. 295 ◽  
Author(s):  
Gavin C. Kennedy ◽  
Iain W. Wilson
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Functional Genomics ◽  
New Technologies ◽  
Expression Profiles ◽  
New Technology ◽  
In Silico Analysis ◽  
Biological Data ◽  
Database Technology ◽  
Biological Data Integration

High-throughput gene expression profiling using microarrays has given plant biologists a powerful new technology to discover gene function and understand cellular processes. Bioinformatics has rapidly developed to deliver the tools necessary to interpret this gene expression data, but opportunities to further exploit the mass of data from hundreds of experiments are becoming dependent upon the use of sophisticated database repositories. Data mining of these resources will allow plant biologists to compare and link expression profiles and experimental factors to uncover functions and processes that would not normally be visible from analysing a small set of microarray experiments. This in-silico analysis will become critical when designing new experiments and interpreting new results. Consequently microarray databases and their ongoing development are now as important to plant functional genomics as the initial microarray data capture and analysis tools. In order for plant biologists to grasp these new opportunities, an appreciation of microarray database technology and future developments in biological data integration is required. The challenge for plant functional genomics is to embrace these new technologies lest the opportunities for significant discoveries be lost.

Applications for Data Mining Techniques in Customer Relationship Management

2011 ◽  
pp. 188-192
Author(s):  
Natalie Clewley ◽  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
New Technologies ◽  
Relationship Management ◽  
Customer Relationship ◽  
Future Research ◽  
Supporting Evidence ◽  
Business Decisions ◽  
Data Mining Techniques ◽  
Hidden Knowledge

With the explosion in the amount of data produced in commercial environments, organizations are faced with the challenge of how to collect, analyze, and manage such large volumes of data. As a consequence, they have to rely upon new technologies to efficiently and automatically manage this process. Data mining is an example of one such technology, which can help to discover hidden knowledge from an organization’s databases with a view to making better business decisions (Changchien & Lu, 2001). Data mining, or knowledge discovery from databases (KDD), is the search for valuable information within large volumes of data (Hand, Mannila & Smyth, 2001), which can then be used to predict, model or identify interrelationships within the data (Urtubia, Perez-Correa, Soto & Pszczolkowski, 2007). By utilizing data mining techniques, organizations can gain the ability to predict future trends in both the markets and customer behaviors. By providing detailed analyses of current markets and customers, data mining gives organizations the opportunity to better meet the needs of its customers. With such significance in mind, this chapter aims to investigate how data mining techniques can be applied in customer relationship management (CRM). This chapter is organized as follows. Firstly, an overview of the main functionalities data mining technologies can provide is given. The following section presents application examples where data mining is commonly applied within the domain, with supporting evidence as to how each enhances CRM processes. Finally, current issues and future research trends are discussed before the main conclusions are presented.

Big Data Analytics

2019 ◽  
pp. 77-108
Author(s):  
Pratiyush Guleria ◽  
Manu Sood
Keyword(s):  
Data Mining ◽  
Big Data ◽  
New Technologies ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Educational Systems ◽  
Related Data ◽  
Digital Transactions ◽  
The Right

Due to an increase in the number of digital transactions and data sources, a huge amount of unstructured data is generated by every interaction. In such a scenario, the concepts of data mining assume great significance as useful information/trends/predictions can be retrieved from this large amount of data, known as big data. Big data predictive analytics are making big inroads into the educational field because with the adoption of new technologies, new academic trends are being introduced into educational systems. This accumulation of large data of different varieties throws a new set of challenges to the learners as well as educational institutions in ensuring the quality of their education by improving strategic/operational decision-making capabilities. Therefore, the authors address this issue by proposing a support system that can guide the student to choose and to focus on the right course(s) based on their personal preferences. This chapter provides the readers with the requisite information about educational frameworks and related data mining.

The Issue of Missing Values in Data Mining

2011 ◽  
pp. 1102-1109
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Data Mining ◽  
Missing Data ◽  
Incomplete Data ◽  
Missing Values ◽  
Large Data ◽  
Original Data ◽  
Customer Relationship ◽  
Data Sets ◽  
Data Mining Technique ◽  
Data Set

The essence of data mining is to investigate for pertinent information that may exist in data (often large data sets). The immeasurably large amount of data present in the world, due to the increasing capacity of storage media, manifests the issue of the presence of missing values (Olinsky et al., 2003; Brown and Kros, 2003). The presented encyclopaedia article considers the general issue of the presence of missing values when data mining, and demonstrates the effect of when managing their presence is or is not undertaken, through the utilisation of a data mining technique. The issue of missing values was first exposited over forty years ago in Afifi and Elashoff (1966). Since then it is continually the focus of study and explanation (El-Masri and Fox-Wasylyshyn, 2005), covering issues such as the nature of their presence and management (Allison, 2000). With this in mind, the naïve consistent aspect of the missing value debate is the limited general strategies available for their management, the main two being either the simple deletion of cases with missing data or a form of imputation of the missing values in someway (see Elliott and Hawthorne, 2005). Examples of the specific investigation of missing data (and data quality), include in; data warehousing (Ma et al., 2000), and customer relationship management (Berry and Linoff, 2000). An alternative strategy considered is the retention of the missing values, and their subsequent ‘ignorance’ contribution in any data mining undertaken on the associated original incomplete data set. A consequence of this retention is that full interpretability can be placed on the results found from the original incomplete data set. This strategy can be followed when using the nascent CaRBS technique for object classification (Beynon, 2005a, 2005b). CaRBS analyses are presented here to illustrate that data mining can manage the presence of missing values in a much more effective manner than the more inhibitory traditional strategies. An example data set is considered, with a noticeable level of missing values present in the original data set. A critical increase in the number of missing values present in the data set further illustrates the benefit from ‘intelligent’ data mining (in this case using CaRBS).

Pemanfaatan Metode K-Means Dalam Penentuan Persediaan Barang

2018 ◽  
Vol 6 (1) ◽  
pp. 41-48
Author(s):  
Santoso Setiawan
Keyword(s):  
Data Mining ◽  
Stock Management ◽  
Transaction Data ◽  
Business People ◽  
Cluster Data ◽  
Data Clusters ◽  
Inventory Strategy ◽  
Transactional Data

Abstract   Inaccurate stock management will lead to high and uneconomical storage costs, as there may be a void or surplus of certain products. This will certainly be very dangerous for all business people. The K-Means method is one of the techniques that can be used to assist in designing an effective inventory strategy by utilizing the sales transaction data that is already available in the company. The K-Means algorithm will group the products sold into several large transactional data clusters, so it is expected to help entrepreneurs in designing stock inventory strategies.   Keywords: inventory, k-means, product transaction data, rapidminer, data mining   Abstrak   Manajemen stok yang tidak akurat akan menyebabkan biaya penyimpanan yang tinggi dan tidak ekonomis, karena kemungkinan terjadinya kekosongan atau kelebihan produk tertentu. Hal ini sangat berbahaya bagi para pelaku bisnis. Metode K-Means adalah salah satu teknik yang dapat digunakan untuk membantu dalam merancang strategi persediaan yang efektif dengan memanfaatkan data transaksi penjualan yang telah tersedia di perusahaan. Algoritma K-Means akan mengelompokkan produk yang dijual ke beberapa cluster data transaksi yang umumnya besar, sehingga diharapkan dapat membantu pengusaha dalam merancang strategi persediaan stok.   Kata kunci: data transaksi produk, k-means, persediaan, rapidminer, data mining.

DATA MINING AS A MEANS OF QUALITY CONTROL IN PRODUCTION AND ORGANIZATION MANAGEMENT

2020 ◽  
Vol 1 (5) ◽  
pp. 130-138
Author(s):  
L. S. ZVYAGIN ◽  
Keyword(s):  
Data Mining ◽  
Quality Control ◽  
New Technologies ◽  
Data Interpretation ◽  
Organization Management

The article deals with data mining (IAD), which is widely used both in business and in various studies. IAD methods are used to create new ways to solve problems of forecasting, segmentation, data interpretation, etc. The problems to be solved by creating new technologies and methods of IAD are analyzed.

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