Data Mining (the analysis step of the Knowledge Discovery in Databases process or KDD), a relatively young and interdisciplinary field of computer science, is the process of discovering or extracting new patterns from large data sets involving methods from statistics and artificial intelligence. It is commonly used in marketing, surveillance, fraud detection, scientific discovery and now gaining wide way in social networking. Anything and everything on the Internet is fair game for extreme data mining practices. Social media covers all aspects of the social side of the internet that allow us to get contact and carve up information with others as well as intermingle with any number of people in any place in the world. This paper uses the dataset “Social side of the Internet” from Pew Research Center. The focus of the research is towards exploration on impact of the internet on social group activities using Data Mining Techniques. The original dataset contains 162 attributes which is very large and hence the essential attributes required for the analysis are selected by feature reduction method. The selected attributes were applied to Data Mining Classification Algorithms such as RndTree, ID3, K-NN, C-RT, CS-CRT, C4.5 and CS-MC4. The Error rates of various classification Algorithms were compared to bring out the best and effective Algorithm suitable for this dataset.
1.1 Background of the Study
Data Mining means extracting hidden information from the database. Database may be numerical, categorical or textual type. Depending upon the database, we have different terms to define the field. If we discuss more about textual database for finding hidden information, we are termed as text miner. Similarly, if we simply talk about mining of information from simple table, consisting of rows and columns, it can be termed as simply data mining, although the term data mining is broad because it covers not only a relation but also amass of data except those tables.
For the Data Mining purpose, many more algorithms are developed by scientists. These algorithms are basically not equally workable for heterogeneous type of data.
Homogenous type of data may be preferable for that algorithm. And the philosophy of development of algorithm is also different in different scenario.
The operations performed in data mining purpose are of different type- classification, clustering, association, ranking, et cetera. Classification means categorizing the instances in different perspectives i.e., distance based, probabilistic based or rule based, whereas, clustering means making a group of data to form cluster so that inter-cluster similarity is high in comparison to intra-cluster similarity and association means likelihood probability.
The data mining algorithm and their functions can be illustrated in a much clearer manner in Fig. 1.
In Fig. 1, the database is given in a big drum-like diagram. The pattern of the database instances is user’s question and she is trying to find it with the application of the database algorithm. For finding a pattern, it can be clustered, which we can term as clustering.
The telecommunications industry generates and stores a tremendous amount of data (Han et al, 2002). These data include call detail data, which describes the calls that traverse the telecommunication networks, network data, which describes the state of the hardware and software components in the network, and customer data, which describes the telecommunication customers (Roset et al, 1999). The amount of data is so great that manual analysis of the data is difficult, if not impossible. The need to handle such large volumes of data led to the development of knowledge-based expert systems. These automated systems performed important functions such as identifying fraudulent phone calls and identifying network faults. The problem with this approach is that it is time consuming to obtain the knowledge from human experts (the “knowledge acquisition bottleneck”) and, in many cases; the experts do not have the requisite knowledge. The advent of data mining technology promised solutions to these problems and for this reason the telecommunications industry was an early adopter of data mining technology (Roset et al, 1999).
Telecommunication data pose several interesting issues for data mining. The first concerns scale, since telecommunication databases may contain billions of records and are amongst the largest in the world. A second issue is that the raw data is often not suitable for data mining. For example, both call detail and network data are time-series data that represent individual events. Before this data can be effectively mined, useful “summary” features must be identified and then the data must be summarized using these features. Because many data mining applications in the telecommunications industry involve predicting very rare events, such as the failure of a network element or an instance of telephone fraud, rarity is another issue that must be dealt with. The fourth and final data mining issue concerns real-time performance because many data mining applications, such as fraud detection, require that any learned model/rules be applied in real-time (Ezawa& Norton, 1995). Several techniques has also been applied is tackling all these issues in telecommunication companies.
Telecommunication networks are extremely complex configurations of equipment, comprised of thousands of interconnected components. Each network element is capable of generating error and status messages, which leads to a tremendous amount of network data. This data must be stored and analyzed in order to support network management functions, such as fault isolation. This data will minimally include a timestamp, a string that uniquely identifies the hardware or software component generating the message and a code that explains why the message is being generated. For example, such a message might indicate that “controller 7 experienced a loss of power for 30 seconds starting at 10:03 pm on Monday, May 12.”
Due to the enormous number of network messages generated, technicians cannot possibly handle every message. For this reason expert systems have been developed to automatically analyze these messages and take appropriate action, only involving a technician when a problem cannot be automatically resolved (Weiss, Ros&Singhal, 1998).
1.2 Statement Of The Problem
1.3 Objectives of the Study
The following are the objectives of this study:
• To provide an overview on data mining.
• To examine the various data mining techniques and algorithms
• To identify the challenges of data mining faced by organization in Nigeria
1.4 Research Questions
• What is data mining?
• What are the various data mining techniques and algorithms?
• What are the challenges of data mining faced by organization in Nigeria?
1.5 Significance of the Study
The following are the significance of this study:
• The outcome of this study will educate on data mining techniques and algorithms of organization in Nigeria, the data mining applications and how they can be used in fraud detection.
• This research will be a contribution to the body of literature in the area of the effect of personality trait on student’s academic performance, thereby constituting the empirical literature for future research in the subject area.
1.6 Scope of the Study
This study will cover various data mining techniques and algorithms used by organization in Nigeria.
1.7 Limitation of the Study
Financial constraint- Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).
Time constraint- The researcher will simultaneously engage in this study with other academic work. This consequently will cut down on the time devoted for the research work.