Computer Science Research Paper

               International Journal of Computer Applications (0975 – 8887)
                                                      Volume 15– No.7, February 2011


          A Review on Data mining from Past to the Future

Venkatadri.M                                  

Research Scholar,

Dept. of Computer Science,

Dravidian University, India.

                              

Dr. Lokanatha C. Reddy

Professor,

Dept. of Computer Science,

Dravidian University, India.

ABSTRACT

Data and Information or Knowledge has a significant role on human activities. Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information. Due

to the importance of extracting knowledge/information from the large data repositories, data mining has become an essential component in various fields of human life. Advancements in Statistics, Machine Learning, Artificial Intelligence, Pattern Recognition and Computation capabilities have evolved the present day’s data mining applications and these applications have enriched the various

fields of human life including business, education, medical, scientific etc. Hence, this paper discusses the various improvements in the field of data mining from past to the present and explores the future trends.

1. INTRODUCTION

The advent of information technology in various fields of human life has lead to the large volumes of data storage in various formats like records, documents, images, sound recordings, videos, scientific data, and many new data formats. The data collected from different applications require proper mechanism of extracting knowledge/information from large repositories for better decision making. Knowledge discovery in databases (KDD), often called data mining, aims at the discovery of useful information from large collections of data. The core functionalities of data mining are applying various methods and algorithms in order to discover and extract patterns of stored data. From the last two decades data mining and knowledge discovery applications have got a rich focus due to its significance in decision making and it has become an essential component in various organizations.

2. HISTORICAL TRENDS OF DATA MINING

The building blocks of data mining is the evolution of a field with the confluences of various disciplines, which includes database management systems(DBMS), Statistics, Artificial Intelligence(AI), and Machine Learning(ML). The era of data

mining applications was conceived in the year1980 primarily by research-driven tools focused on single tasks [3]. The early day’s data mining trends are as under.

2.1 Data Trends

In initial days, data mining algorithms work best for numerical data collected from a single data base, and various data mining techniques have evolved for flat files, traditional and relational databases where the data is stored in tabular representation. Later on, with the confluence of Statistics and Machine Learning techniques, various algorithms evolved to mine the non numerical data and relational databases.

2.2 Computing Trends

The field of data mining has been greatly influenced by the development of fourth generation programming languages and various related computing techniques. In, early days of data mining most of the algorithms employed only statistical techniques. Later on they evolved with various computing techniques like AI, ML and Pattern Reorganization. Various data mining techniques (Induction, Compression and Approximation) and algorithms developed to mine the large

volumes of heterogeneous data stored in the data warehouses.

3. CURRENT TRENDS

The field of data mining has been growing due to its enormous success in terms of broad-ranging application achievements and scientific progress, understanding. Various data mining applications have been successfully implemented

in various domains like health care, finance, retail, telecommunication, fraud detection and risk analysis...etc.

3.1 Mining the Heterogeneous data

The following table depicts various currently employed data mining techniques and algorithms to mine the various data formats in different application areas. The various data mining

areas are explained after the table1.

3.2 Utilizing the Computing and Networking Resources

Data mining has been prospered by utilizing the advanced computing and networking resources like Parallel, Distributed and Grid technologies. Parallel data mining applications have evolved using the Parallel computing, typical parallel data mining applications employ the Apriori algorithm. Parallel computing and distributed data mining are both integrated in Grid technologies . Grid based Support Vector Machine method is used in distributed data mining. Recently, various soft computing methodologies have been applied in data mining such as fuzzy logic, rough set, neural networks, evolutionary computing (Genetic Algorithms and Genetic Programming), and support vector machines to analyze various formats of data stored in distributed databases results in a more intelligent and robust system providing a human-interpretable, low cost, approximate solution, as compared to traditional techniques [15] for systematic analysis, a robust preprocessing system, flexible information processing, data analysis and decision making.

3.3 Research and Scientific Computing Trends

The explosion in the amount data from many scientific disciplines, such as astronomy, remote sensing, bioinformatics, combinatorial chemistry, medical imaging, and experimental physics are tuning to various data mining techniques, to find out useful information.

3.4 Business Trends

Today’s business must be more profitable, react quicker and offer high quality services that ever before. With these types of expectations and constraints, data mining becomes a

fundamental technology in enabling customer’s transactions more accurately.

4. FUTURE TRENDS

Due to the enormous success of various application areas of data mining, the field of data mining has been establishing itself as the major discipline of computer science and has shown interest potential for the future developments. Ever increasing technology and future application areas are always poses new challenges and opportunities for data mining, the typical future trends of data mining includes:

·        Standardization of data mining languages

·        Data preprocessing

·        Complex objects of data

·        Computing resources

·        Web mining

·        Scientific Computing

·        Business data                      

5. COMPARATIVE STATEMENT

The following table presents the comparative statement of various data mining trends from past to the future.

6. CONCLUSION

In this paper we the various data mining trends are reviewed from its inception to the future. This review would be helpful to researchers to focus on the various issues of data mining.