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PROJECT TOPIC:  DEVELOPING A DOCUMENT CLUSTRING SYSTEM ON BIG DATA
Department:  Computer Science
AMOUNT:  20000
FORMART:   MS WORD
PAGES:  82 pages, abstract, chapter 1-5 , APENDIX A source code and APENDIX B output, well reserached and supervised
  Algorithm

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Abstract

The rate of data creation at present has increased so much that 90% of the data in the world today has been created in frequent times which posed challenges to users. The clustering is one of the important data mining issues especially for big data analysis, where large volume data should be grouped together, nowadays technologies are able to store and process data ever a larger and larger amount of data. The data of this kind are called big data. Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using traditional data processing tools. This huge amount of data is being viewed by business organizations and researchers as a great potential resource of knowledge that needs to be discovered due to its high volume continuous increment. In this, case Traditional methods of data analysis and management do not suffice.

In this research work we proposed k-means clustering algorithm as an efficient tool that will be used to solve the clustering problem as stated above to replace the traditional means of data analysis and management especially in big data.

The proposed system designed and implemented using an object oriented programming language PHP5, HTML5, CSS3, and MSQL data base. The proposed system will help users to experience efficient big data clustering in data analysis and management on big data.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

The development of Internet technologies and social media revolution has increased the speed, and the amount of data produced on daily bases. Companies and business organisations have started to collect more data than they know what to do with. Hence, Big Data has rapidly increased in different application areas and tends to dominate future technologies. (Zeng, G 2012,)

Big data can be defined as high-volume, high velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. This type of huge volume of data of course brings several challenges. Several challenges include analysis, retrieval, search, storage, sharing, visualization, transfer and security of data. Data generation capacity has never been so big, every day 2.5 trillion bytes of data are generated and 90% of produced data in the world so far were produced in the last two years. However, the data are not in a meaningful form they are in the raw form. It is necessary to process them to explore patterns, hence transforming them to meaningful information. If they are used in an accurate manner, Big Data have the ability to give results on these massive data piles, which shed light to future technologies ( Biswas and Jacobs, 2012).

Doing so, data mining methodologies play an important role to explore data,

Data mining is defined as the science of extracting useful information from large datasets or databases. This area combines several disciplines such as statistics, machine learning, artificial intelligence, pattern recognition, and data management. One of these challenges constitutes a wide research area in data Management. The idea is to build computer programs that examine the databases automatically, search for rules or patterns in order to make accurate predictions about future Data. Since actual data are usually imperfect, it is expected that there will be problems, exceptions to each rule.

Therefore, the algorithms must be robust enough to adapt to the imperfect data and extract regularities that are inaccurate, but useful. One of the most important problem sources in Big Data and data mining is still the heterogeneity of data structure and data resources when gathering data. This is why understanding and examination of these structural features of Big Data have impact on the effectiveness and efficiency of data mining.

Traditional methods of data analysis and management do not suffice. New technologies to deal with this data called Big Data are required. The term Big Data refers to large-scale information management and analysis technologies that exceed the capability of traditional data processing technologies. The incorporation of Big Data is changing Business Intelligence and Analytics by providing new tools and opportunities for leveraging large quantities of structured and unstructured data.

Big Data is notable not because of its size, b


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