Decision Tree Induction & Clustering Techniques in Sas Enterprise Miner, Spss Clementine, and Ibm Intelligent Miner – a Comparative Analysis

15) Which of the following statements the use of decision trees in multi-stage decision making problems is FALSE?

Kudler is looking for ways to increase sales and customer satisfaction. To achieve this goal Kudler will use data mining tools to predict future trends and behaviors to allow them to make proactive, knowledge-driven decisions. Kudler’s marketing director has access to information about all of its customers: their age, ethnicity, demographics, and shopping habits. The starting point will be a data warehouse containing a combination of internal data tracking all customers contact coupled with external market data

The predicted class is the mode of the output of the individual decision trees. A sample is selected at random from the training set (with replacement) and a decision tree is constructed to fit the sample. The splits are performed on a random subset of features instead of choosing the best split among all features.

Decision making refers to the process of finding and selecting options according to the priorities and values of the person making the decision. Since there are many choices involved, it is important to identify as many options as possible so as to pick the option that best fits a company’s target, goals, values and vision. Due to the integral role of decision making in company growth and financial progress, many firms such as Amazon.com and EBay are pumping in huge investments in business intelligence systems, which are made up of certain technological tools and technological applications that are created for the purpose of facilitating improved decision making process in

According to de Ville & Neville (2013), a decision tree can be defined as “a simple, but powerful form of multiple variable analysis.” Decision Trees were first introduced over fifty years ago, and are still being refined today to provide new functionality for dealing with the newer code-development related issues we might encounter (de Ville & Neville, 2013). Since decisions trees are essentially binary trees, they also operate in O(log(n)) time complexities for best and worst case scenarios, and O(n) for space. Decision trees are generated by algorithms which determine possible ways to branch-off data depending on the end result of a set question. From this, the aim of the tree would be to predict the probability of a specific outcome. In terms of Space Quest: where will the player be next, and how can they be reached? For each node in the implementation, the AI will ask itself if the defined condition mentioned earlier yielded either a true or false

These trees are a more flexible non-parametric option to survival methods such Cox’s proportional hazards methods and AFT models with more stringent assumptions. The main difference in the various predictive trees is the splitting criteria.

The key aim of this project is to develop an information system based on data mining techniques to build upon existing customer relationships and increase profit. Part & Parcel Computers has been at the forefront of the computer parts industry for the past fifteen years. They have developed a reputation for the cheapest computer parts by focussing on a cost-leadership strategy. P&P computers have a loyalty card programme that provides discounts and benefits to its customers but has not used this collected data to specifically identify and target its loyal customers. Unless P&P computers build sales volume with the data, it is merely an overhead without any tangible benefit (Cox, 2012).

Data Mining is a technique used in various domains to give meaning to the available data and different types of Data to be handled like numerical data, non-numeric data, image data...etc. In classification tree modelling the data is classified to make predictions about new data. Using old data to predict new data has the danger of being too fitted on the old data. In this we evaluated different types of data to be collected from UCI repository for classify the data using the different classification algorithms J48, Naive Bayes, Decision Tree, IBK. This paper evaluates the classification accuracy before applying the feature selection algorithms and comparing the classification accuracy after applying the feature selection with learning algorithms.

Many researchers have proposed various methodologies for finding best solution. J. Ross Quinlan. In machine learning community, the decision tree algorithms, Quinlan’s ID3 and its successor C4.5: Programs for machine learning are probably the most popular. The various issues related to decision tree are discussed from the initial state of building a tree to methods of pruning, converting trees into rules and handling other problems such as missing attribute values. Apart from that, Quinlan discusses limitations of programs for machine learning, such as its bias in favour of rectangular regions along with ideas for extending the abilities of algorithm. [1]

A LGORITHM D ESCRIPTION Decision Tree (DT) is used to build regression or classification models in the form of a tree structure. It predicts the value of a target variable based on simple decision rules inferred from the data features. It breaks down the dataset into smaller subsets while concurrently an associated decision

In this paper we are trying to understand how specific company software can provide better information to users, improve the business process (sales), etc. by incorporating data mining and data warehouse concepts to their existing

2. Classification stage – applying the algorithm on the dataset to get FDT (Fuzzy Decision Tree) and analyse them to get results.

[1] Jaiwei Han and Micheline Kamber “Data Mining: Concepts and Techniques”,Morgan Kaufmann Publications Second Edition,2006

Data mining is the process used to analyse large quantities of data and gather useful information from them. It extracts the hidden information from large heterogeneous databases in many different dimensions and finally summarizes it into categories and relations of data. Clustering and classifications are the two main techniques of data mining followed by association rules, predictions, estimations and regressions. Many fields imply on data mining like games, business, surveillance, science and engineering etc.

Given a positive number K and an unknown sample, a KNN classifier searches the K closest observations in training set to the unknown sample. It then classifies the unknown sample into the class with the smallest distance. The advantage of KNN is that it does not need to estimate the relationship between the response and the predictors (Shmueli, et al. 2016), while this method is dramatically affected by the number of Nearest Neighbors (James, et al. 2013).