Supervised Data Mining For Business Intelligence: Techniques And Applications Essay.

The content of your technical report should include the following A-H aspects:

1. A meaningful title (5-20 words) followed by your name and student ID

Title describe your topic and point out your research direction, so it is very important and we list the tips below.

Tip 1: The title may be narrowed down further from the title of your Assignment 1.

Tip 2: Make the best use of Practicals to implement your data analysis project.

First, please learn a framework of basic skills of using Weka to prepare, process and analyze data in Practicals 1-3 and focus on Practical 6 (Performance Evaluation). Practical 6 comprises the skills to compare multiple data mining methods in different metrics, which directly helps your comparison and evaluation of different techniques in your technical report.

Then, You can focus on one of the following techniques for analysing different types of data according to your topic:

• Practical 4 or Practical 5 for processing general numeric data

• Practical 7 Predicting Time Series

• Practical 8 Text Mining

• Practical 9 Image Analysis

• Others learnt by yourself

Practical 10 Recommendation provides an application example using techniques in Practicals 4-9.

Importance of the Title

Many people have been attracted with the increase in web technology and the use of social network to present their ideas and the working of their web projects and services. The online social networks have become more reach with information purpose that is inclusive for event such as data sharing. Due to this, business intelligence tools are becoming more powerful online tool to make companies more comfortable. The Implementation of Supervised Data Mining is one factors that has attracted business owner’s as well a huge client base. Due to this the technique has become the most popular micro blogging social sites where users are free share their view, and their aspirations. By using supervised data mining, business are able to get the latest trending new from their customer about the attest product.

In this particular paper, statistical approach that is used to supervised data is through the use of the use graph clustering. All the information is tokenized using n-grams techniques by the use of certain allocation method. The project uses Latent Distinct allocation method to identify the significant key terms that are latter used in business generation.  The second method that is also important in this paper is the Markov Cluttering.

Literature Review:

The literature review is basically presented to discuss about business intelligent and supervised way of data mining technology and its understanding.  From several articles on research, business intelligence is very key in decision making in most of the organizations use. In this literature part, the research tend to present a detailed discussion on the intelligence and how the entire idea of business intelligence work to make the work of the managers easier. The data mining in the recent past has being used very much widely and this has caused the amount of data in the field to widen and to mark sense of what is done.

The data handled by the business intelligence handles a large number of data and this provides a strategy for the business and new ideas and opportunities are achieved.  Both structured and unstructured data can easily be handled and this enable us to provide business solutions. Data mining is basically necessary to help the business to provide a prediction of the entire procedure. Business intelligence has also fixed several values that can be able to achieve a goal in putting clear form and format of the business perspective. The theoretical approach in this particular research is to understand a detailed survey in the supervised data mining.

Using Practicals for Data Analysis Projects

The first work that every researcher has to consider before coming up with the data analysis is to make a solid decision on the data he is yet to deal with. In this case, we have number of typologies that has proven to us that supervised data mining is a useful tool in business indigence analysis. One of the method to be used and which is distinctive is the quantitative variables. In this method, we asked how much data is involved. The variable to be used in this case is known as the type of data to be inserted in this entire information and collection. Quantitate method in cost cases can be continuous and discrete.

The method must use quantitative variables and the theory must take certain value that is within a given range. For instance, we can determine the number of businesses that have used data mining for the first six months information and how often this information is used based on the preferences. Data collection must also take place based on the certain value of data that is meant to be gathered. the type of data gathered in this case is row data and must be transferred in a more readable and understand way. The data was collected using a questionnaire and every information coded in a well presented and developed software. After coding the data, graphs and histograms were drown that depicts the data collected.

This section presents functional details for all the unsupervised data mining and approach that is used to get the information about the business. To make the work easy for the users, the methods is presented in a workflow all the methods that are highlighted. One of the method to be used is the information crawling, information tokenization to analyze the business contents and remove unwanted contents.

There are several methods of data collection that will be of important to this research. One such method includes: sampling, one of the method to be used is sampling method. Sampling enable the researchers to gather information from a vast area and to make sure that the information gathered is well understood by the researcher. Secondly the information is also viewed as the first hand information because it comes directly from the people who uses social media on a daily basis.

The choice of data mining techniques was guided by the focus on the most current and the most used models in the market. In this section, the review come up with some of the features and different techniques and determination on how they affect and influence the supervised data. The method does not presents a complete mathematical details of the entire algorithm or even their implementation.  Below are data mining techniques that are used to explain this classification.

Analyzing Different Types of Data

Research has shown that supervised data mining is the best method to perform data mining activities. This is because every user has specific target when performing this type of data mining. The results may be different at different times and the target may be numeric. In real sense supervised data mining may be used when the user is having an ideal subset of data points. When this data is used for building a model such as typical data points even when it is targeting different targets. The supervised data classification is started as the main method.

The classification can be of different types especial when applied to different business. Other method such as regression can also be used to a target value as per the numerical as opposed to performing data categories. All the values must be assessed through the use of the organization in order for the entire data to get all the desired outcomes. The process is also known the predictive data mining because it has capability to proceed the user data and numerical.

Visualization Techniques

Visualization techniques is used because it is a very useful method for discovering partners in the data sets and may be actually used at the begging of the data mining process. In this technique, there is a whole field of research that is dedicated to the search of the projection that the user is interested in.  This projection is also known as the projection pursuit. For instance the cluster are usually numerically represented by different numerical reprobation.

There are a large set of rules when dealing with the structure information classification and hierarchical fashion in the graphical. The visualization will help to discover the meaningful patterns of the good business intelligence and how the information will be supervised. Visualization also classify the entire work into a meaningful interpretable data. This is in line with the goals of visualization which is to permit a wide variety of data mining methods to be used successfully.

Association Rule Techniques.

The association rule will tell us about the association that exist between two or more data. For instance when there are two similar methods that is coming from the managers, the association rule will help the analysis determine the association. The main task determined by association rule is to find out the presence of various items that is within ascertain databases. For us to use this rule successfully, two pieces of information must be put into consideration. The first is to make sure that there is support were the rule lies (Chattamvelli, 2011 pg345). There must be confident level of confident level and how often the rule is correct. The rule is considered to be two-step process. The first step it t find the frequent information from the website and the second if to define each and every items presented. The items in question will occur as frequent as the association of the two tweets to be analyzed.

Literature Review

Neural Network Technique

 The artificial and neural network are called this name because of historical development that stated with the knowledge that machine can be due to this and do things lie human beings. This was possible only if the scientist can find a way to mimic its structure and its functioning the way human being are functioning. To use this techniques to analyst the data, the researcher uses two main techniques and this two techniques corresponds to human brain and link. It also corresponds to the neurons and the human brain at all points. A neuron network in this case would be considered as a connection of all information about the supervised data mining.

When this information is analyzed by the experts, the connection becomes unidirectional. Research has shown that the arrangement of neuron network have a corresponding architecture and different neuron network architecture definitely use different leaning procedures to find the strengths of interconnections. In this regard, there are several number of neural networks and each and every model poses its own strengths as well as weakness when it come to the analysis of the data. All in all the analysis procedure was found effective because the data gathered was accurate and maximum concentration was done to ensure that there is not error in the analysis.

This part demonstrate that use of the three tools to analysis data and come up with meaningful information about the data in question. It is important to note that information given in this case should be relevant and in line with the results. Supervised information extraction is a process that needs so much accuracy because there are so many opinion posted by the users. The users we such the data to analyze should be users who are reliable and the information posted by this users should not be vague. For the purpose of evaluation each and every information given to by the users was labeled and related business were assembled.  A java application is also developed to ensure that the values are well calculated and all the metrics recorded way.

The paper provides a description of selected techniques from the data mining point of view. Evaluation team has noticed that all the data mining techniques had to accomplish the wrk of supervised data in addition the was integrated by the researchers. However, each and every data collected from the website had their own characteristics which is seen to be unique from the rest of the information. It is claimed that there are new research solutions needed for each and every unique problem. The data mining told used has proven itself to be the best told and has better suited the same problem solving techniques. Evaluation team recommends that we should use data mining tool for each and every evaluation and to make sure that every data helps in making the correct decision.

Data Collection and Mining Techniques

Performing such an analysis is has proven to be technical due to unstructured nature of the report and the nature of information that are received from different businesses. Many people presents their own opinion based on the data is to be used in the analysis. Users in most cases strive to express communication but are blocked by the users. One of the key issues that is presented in analyzing data is there classification which is based on the subject in discussion.  Information is normally conceptualized using a different set of significant within the data.  Every events analysis is handled using the key terms and details to make the user and the reader understand what is meant by each and every definition.

According to most companies, the above techniques helps managers to make decision according to the information that is provided and classified. There is no techniques that can be presented and can be made effective apart from simple supervised solution. One reason why the three techniques were chosen is because there can be no one techniques which can be effective and can give the results as expected. The evaluation suggest the three techniques be used in corporation with each other.

Apart from the events and social notification, the supervised data mining is also used for other purposes such as the product marketing, political campaign and market research. Users are also able to express their own opinion about a product without any victimization. Performing certain analysis to business also sport and the emerging issue s in the society is one key factor the assess a public opinion that concerns the events of a considerable interest to various parties such as the government, and the security agencies. Vast and wide relevant information projected by millions of users.

The following observation was deduced from the application it is important that the researchers have their knowledge and the goals of the people who are posting information on the social media. That helped in creating data set and selecting data set as well as focusing on the variable subsets or even data which is yet to be performed. Preparation also needs data cleaning and processing for instance removing noise, or deciding on the best strategies of reducing noise in the data set.

Data reduction was also done to reduce or to remove some of the attributes a process the will help suit the set to the goal. Next is choosing the data mining task. This is determined whether the goal of KDD is well achieved.  After data reduction and isolation, the best algorithm is chooses for the best method to be used for searching patterns in the data. This process also involves deciding the appropriate model and pattern. Finally is data mining for the information and for the representational messages is done. This messages are presented in different formats, and all the rules that are involves are specified accruing to the work that is mean to be achieved in the particular data.

Visualization Techniques

Extracting useful information from the data and information has become very much easier than collecting information. In this regard, many people have adopted sophisticated techniques and as developed those in a multidiscipline field of data mining which is done in a datasets. When adopting this techniques, one of the key issue is to understand that one of the most difficult activity is to chosen to suit a given problem. For this, modeling application, a more generalized way was adopted to ensure that information extraction was done in the correct manner. A generalized data mining approach is found to improve the information extraction and information accuracy as well as cost effectiveness of the information to be taken care of the information.

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