A Research Study On Machine Learning Community, The Decision Tree Algorithms, Quinlan 's Id3 And Its Successor Essay

The big data analytics deals with a large amount of data to work with and also the processing techniques to handle and manage large number of records with many attributes. The combination of big data and computing power with statistical analysis allows the designers to explore new behavioral data throughout the day at various websites. It represents a database that can’t be processed and managed by current data mining techniques due to large size and complexity of data. Big data analytic includes the representation of data in a suitable form and make use of data mining to extract useful information from these large dataset or stream of data. As stated above the big data analytics has recently emerged as a very popular research and practical-oriented framework that implements i) data mining, ii) predictive analysis forecasting, iii) text mining, iv) virtualization, v) optimization, vi) data security, vii) virtualization tools for processing very large data sets. In the implementation of big data applications, new data mining techniques and virtualization are required to be implemented due to the volume, variability, forms and velocity of the data to be processed. A set of machine learning techniques based on statistical analysis and neural networking technology for big data is still evolving but it shows a great potential for solving a big data business problems. Further, a new concept of in-memory database for enhancing the speed for analytic processing is further helping

(a) See decision tree above. (b) Once Monday 's bid is made, Newtone 's optimal strategy is to accept the bid if it is a $3,000,000 bid and reject it if the bid is for $2,000,000. If Monday 's bid is rejected, then accept Tuesday 's bid, regardless of the amount offered. The EMV of this strategy is $2,600,000.

Based on the decisions you are trying to make, there is a decision-making tool designed with an associated process that will allow you to meet your needs. These tools have been designed to make the process easier. In my various findings

Also I am recommending this tool, because the decision tree analysis process, which are of great importance in the industrial production of high quantity standardized products (Shoes, sandals, etc) and lately has gained importance in the low volume production of customized products. Due to the high capital requirements when looking for the opening of a new store, decision tree analysis is of great relevance for the manufacturing business. Because of it, this attracted the attention of the manufacturing industry, who tried to support practical cost-effectively analysis by using the proper profitability models.

Decision Trees are useful tools for helping you to choose between several courses of action.

Data mining: is a process of discovering patterns in large data involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems with the aim of extracting information and transforming it into an understandable structure for further use.

There are several improvement methods are available to improve decision tree performance in terms of accuracy, and modelling time. Since experimenting with every available method is impossible, some of the methods are selected that are proven to increase decision tree performances. Selected improvement methods and their experimental setups are presented in this chapter.

Data stream mining is a stimulating field of study that has raised challenges and research issues to be addressed by the database and data mining communities. The following is a discussion of both addressed and open research issues [19].

Stream data, can be a continuous, potentially infinite flow of information as opposed to finite, statically stored data sets. Besides querying data streams, another important application is to mine data streams for interesting patterns or anomalies as they happen. For data stream applications, the volume of data is usually too huge to be stored on permanent devices or to be scanned thoroughly more than once. Both approximation and the ability to adapt are key ingredients for executing queries and performing mining tasks over rapid data streams. With the help of the data stream generator the user gets information. To apply some data stream approach i.e. using any of the data mining algorithms the user can get the required output. The data are evaluated by single pass algorithm i.e. reads only one time

The paper, Induction of Decision Trees, briefly discusses the history of machine learning algorithms, the decision tree family of algorithms and their various use cases before giving an in depth explanation of the ID3 algorithm. This essay focuses on a couple of central ideas behind induction on decision trees. The first section will contain general background information and context leading up to the creation of decision trees. Once the context is established, there will be a quick introduction into the ID3 algorithm. This will be followed by a more rigorous discussion of the concepts of shannon’s entropy and information gain. Following the discussion on entropy there will be a quick overview on the proposed methods of handling noisy data. The last section will contain a critique of the paper.

Gaining the knowledge about classifiers from data is one of the main jobs in data mining. Most prior and current research in this field is on the stationary environment where a whole dataset is given to the learning algorithm, these set of data are electronically stored and can be retrieved by algorithm several times if essential. Therefore the mark concept which should be learned are stable.

In order to select the most successful and effective decision, it is significant to follow a decision making process which evaluates all alternatives and to conclude the best alternative. The decision making process is divided up into 8 parts. The first step is identifying the problem, followed by identifying decision criteria, which can establish the steps to follow on how to solve the problem or issue. The next step is analyzing the problem and allocating the weights to the criteria. This includes collecting relevant information and trends in order for it to be analyzed according to the complexity and nature of the problem addressed. The

Data mining is extraction of knowledge from high volume of data. In this data stream mining experiment, I have used “sorted.arff” dataset contains 540888 instances and 22 attributes. I have tried two single algorithms and two ensemble algorithms, tested the accidents on road for last 15 years.

ID3 chooses the class attribute comparing real numbers of entropies, the ID3ϭ Approximation is created to handle the case when entropies are close together resulting in difficulty of making the choice while creating the decision tree [3, Page5]. The approximation uses a value called close-information-gain to choose approximate attributes with the previous value [3, Page5] with a small logarithmic decrease in efficiency . bilities). This is solved by introducing the concept of precision levels where computation are done first using

This is a pedagogical algorithm, which extracts the rules in the form of decision trees. This is similar to most of the decision tree algorithms and grows the tree by recursive partitioning. At every step it stores a queue of leaves that can be further expanded to sub trees and this process is repeated until a stopping condition is met. Traditional decision trees methods have a limited number of training observations. So they only have fewer number of observations to decide upon the split and leaf node class labels but on the other hand, Trepan re-labels the original observations to the classifications made by the network. And the relabeled data will be used for the tree growing process. Additionally it can also add extra data points by mimicking the behavior of the network. It uses the network as an oracle to answer the classification queries about the newly generated data points. This way it can make sure that whenever a split node or leaf node class decision is made there are at least S_(min ) number of data points. Where S_(min )is a user specified number. Whenever we generate new data points at any particular node, we have to make sure that they satisfy all the constraints from root to the current node. One of the approach to distribute the data points over a network is to employ uniform distribution, but Trepan takes into account the distribution of data i.e. at each node it estimates the marginal distribution of data. If the data at the input is continuous then it