This project implements the ID3 algorithm for reading data stored in multiple data sources. It comes under the broader topic of data mining. Data mining is the reading and processing of useful data from different sources. Essentially, the process of hunting for required or useful data contained in a large database is characterized as data mining. In the case of logical outcomes, a decision tree is predominantly used for analysis. The advantages of using a decision tree are that it is easier to model, analyse, and manipulate accordingly. The ID3 algorithm is used to generate a decision tree from a certain set of data.
The ID3 algorithm constructs a decision tree depending on the given dataset. The branches and nodes are characterized by specific logical outcomes featured in the dataset. The speaker identifies two important terms: information gain, and entropy. Entropy is derived from Information Theory and is described as the average of the information embedded in each message at the receiver. Informally, entropy is intuitively understood as impurity and the information content is directly proportional to entropy. This means that, higher the entropy, the information content is higher. The change in information entropy from one distinct state to another is termed as information gain. The aim of constructing a decision tree is to find the attribute that returns the highest information gain.
The presenter explains that the ID3 algorithm accepts training data and attributes list as input and returns a decision tree as output. The procedure for the ID3 algorithm may be summarised in the following points. Initially, the entropy is calculated for each attribute in the dataset. The attribute with minimum entropy is used as reference and ...
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... It is commonly utilised by the machine learning community for learning and analysing algorithms and as a source of data sets.
The implementation involves an example of “Whether to play Tennis”. It consists of various factors such as temperature, humidity, and weather. Each attribute is tagged to a row number termed as “rownum”. Based on the combinations of the different factors, a column of “Whether to play Tennis” has a binary option of “Yes” or “No”.
The speaker then concludes the presentation by stating that this project builds a decision tree using the ID3 algorithm and derives a set of rules. The primary focus is for data stored across multiple SQL server databases. It is also worthy to mention the importance of validating the attributes and pruning the decision tree for a complex model. Results may not possess coherence if these factors are not taken care of.
... middle of paper ... ... In Intelligent Data Engineering and Automated Learning–IDEAL 2006 (pp. 1346-1357. Springer Berlin, Heidelberg.
Rueter, M., & Fields, E. (2012). Tableau for the enterprise:an overview for it. In Retrieved from http://www.tableausoftware.com/learn/whitepapers
Strengths: Very flexible with very few limits to the analysis, able to handle empirical distributions, can be easily adapted and extended, very intuitive and easily understood, computationally tractable when the dimensions of uncertainty increase
In 1980, James Anderson’s paper, Computer Security Threat Monitoring and Surveillance, bore the notion of intrusion detection. Through government funding and serious corporate interest allowed for intrusion detection systems(IDS) to develope into their current state. So what exactly is IDS? An IDS is used to detect malicious network traffic and computer usage through attack signatures. The IDS watches for attacks not only from incoming internet traffic but also for attacks that originate in the system. When a potential attack is detected the IDS logs the information and sends an alert to the console. How the alert is detected and handled at is dependent on the type of IDS in place. Through this paper we will discuss the different types of IDS and how they detect and handle the alerts, the difference between a passive and a reactive system and some general IDS intrusion invasion techniques.
indicates towards a fraud. On eof the most important qualities or benefits of this model is that it understands the pattern in the data and generates the result. Once the result is generated the model checks as to how close was the result from the actual results. Based on this analysis the model adjusts its weights to give an accurate result the next time. Once this model has been trained to give accurate results, it can be used to analyze other data as well. Even when Neural Networks are widely accepted, they are not really used that much in the marketing industry merely by the fact that data preparation for this model is very complex time consuming as compared to the Regression Analysis. The marketers are much comfortable using the Regression Analysis over Neural Networks because of the ease of interpreting the results in the Regression Analysis.
...ferred because it produces meaningful information about each data point and where it falls within its normal distribution, plus provides a crude indicator of outliers. (Ben Etzkorn 2011).
Although Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) have been grouped together here (IDPS), there are distinctions between them. On the most basic level, both will monitor the network...
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
[7] Elmasri & Navathe. Fundamentals of database systems, 4th edition. Addison-Wesley, Redwood City, CA. 2004.
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
Big data originated with web search companies that encountered problems with querying large amounts of both structured and unstructured data. With regard to its background, “big data came into being when web search companies developed ways to perform distributed computing on large data sets on computer clusters” Floyer (2014: 1). Big data then spread to enterprises due to their adoption of developing, processing and dissemination of data.
It simplifies the storage and processing of large amounts of data, eases the deployment and operation of large-scale global products and services, and automates much of the administration of large-scale clusters of computers.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
Nowadays, the information is the most treasured asset in an organization, due to it along with the experience represents the input necessary to take appropriate decisions and consequently to have success in the business. Almost all the information and knowledge related with the processes business, goods and services offered by a company, is processed, managed and stored through technology and information systems, thus the security of information has become increasingly important and plays a critical role in the enterprise government.