Decision tree is a commonly used classifier. The decision tree algorithm is simple, non-parametric, computationally faster and easy to interpret. Decision tree classification has been done in vibration based gearbox fault diagnosis and was found to give good results. Decision tree has a great potential in deriving the rules from the feature set . Decision tree reduces the domain knowledge required by identifying the relevant features .
Recently, a number of clustering algorithms have been proposed to select a cluster-head, based on various parameters such as speed and direction, mobility, energy, position, and the number of neighbours of a given node. Though these works have many advantages, there are certain limitations like high computational overheads for both clustering algorithm execution and update operations. Hussein,A.H, et al have proposed the Highest-Degree Algorithm, also known as Connectivity-Based Algorithm ,in which the degree of nodes is assumed to be the number of neighbours of a given node. The major drawbacks of this algorithm are the degree of a node changes rapidly and the cluster-heads are not likely to play their role as cluster-heads for very long
Because it uses a quick cluster algorithm upfront, it can handle large data sets that would take a long time to compute with hierarchical cluster methods. In this respect, it combines the best of both approaches. Also two-step clustering can handle scale and ordinal data in the same model. Two-step cluster analysis also automatically selects the number of clusters, a task normally assigned to the researcher in the two other
current reality tree (CRT) that uses sufficiency logic to document the cause-effect relationships responsible for a system's current state. In regard to the second query develops another TOC logic tool, a future reality tree (FRT), to help construct and evaluate planned interventions for resolving the core conflict and improving effectiveness. The paper concludes by stating that feasibility and utility of using TOC TP logic tools helps managers of service organizations to improve their overall system performance. Kim et al. (2008) stated by drawing conclusions relative to the feasibility and utility of using TOC TP logic tools to help managers of service organizations improve their overall system performance.
You will also be able to explain Cluster Management. Let us start with the first topic in the following screen. Slide 3: Overview of Hadoop cluster A Hadoop cluster is a particular case of computational cluster designed specifically for storing and analysing vast quantities of
In the organizational process model, it is imperative that decision makers weigh all aspect of their business. This decision making technique ensures every group member has equal input and encourages all participants to voice their opinion while preventing the domination of discussion of a single person. The lead facilitator of this technique would be the accounting manager since the decision will affect the financial state of the organization. It is assumed that important financial decisions concerning hiring more personnel to ensure the growth of the company would include the ideas of participant’s top managerial positions. Nominal group technique requires organizations to follow certain steps.
A large number of classification models have been developed for predicting future trends of stock market indices and foreign exchange rates. 3) Clustering analysis segments a large set of data into subsets or clusters. Each cluster is a collection of data objects that are similar to one another within the same cluster but dissimilar to objects in other clusters. In other words, objects are clustered based on the principle of maximizing the intra-class similarity while minimizing the inter-class similarity. For example, clustering techniques can be used to identify stable dependencies for risk management and investment management.
This paper also provides a set of financial indicators which can be used in the assessment of the credit risk rating. By using the proposed model, decision makers will be able to determine the class of credit risk rating in commercial banks, and receive remedial advices for solving the bank financial problems and enhancing the financial indicators values. Fuzzy logic technique is one of the most important machine learning techniques used to predict credit risk rating in commercial banks. The results from this study showed that fuzzy logic is scalable, reliable, stable, and different from classical methods and based on natural language. It is recommended as a future work to integrate other machine learning techniques such as neural networks with the proposed model in order to enhance the accuracy of the model
2 DYPCOE, Akurdi, Pune Business Intelligence and Data Mining Pattern Mining If = h(ab); di and = h(abc); (de)i; where a, b, c, d, and e are items, then is a sub sequence of and is a super sequence of : A sequence database, S, is a set of tuples, hSID; si where SID is a sequence ID and s is a sequence. Customer Sequence : List of customer transactions ordered by increasing trans- action time. A Customer support a sequence if the sequence is contained in the customer-sequence Support for sequence : Fraction of Total Customers that sup- ports a sequence. Maximal Sequence : A sequence that is not contained in any other sequence Large sequence : Sequence that meets mini-support . 2.1 Example Consider the sequence database, S, given in Figure 2.1.
The diagram shows a cluster with two nodes which are connected or interconnected all together by the means of a high-speed link which are use to exchange messages or data in other to manage the activities of the cluster. These links between the nodes can be shared using LAN with the help of other computers, which are not use as part of the cluster (Stallings 2013:655). Also showed in the diagram in the configuration of b, there is a disk use to connect to multiple computers in the cluster and there is a high-speed link of interconnection of the clusters. RAID is the name of the disk used. This type of disk is used when if there is a failed node, it does not conciliation the whole system.