The discriminant function is a linear variate of metric measurements of two or more independent variables, which are used to explain or forecast a dependent variable. The only difference is that discriminant analysis is apt for researching problems in, which the dependent variable is nominal or nonmetric (categorical). In regression it is utilised when the dependent variable is considered to be metric. Logistic regression is a variant of regression, which has many similarities except for the type of dependent variable. Discriminant analysis can also be compared to “reversing” multivariate analysis of variance also known as (MANOVA).
The mining function must be specified when a model is created. A mining function refers the methods for solving data mining problems. This mining function is required for CRETAE_MODEL argument. Mining functions Description Association Is a descriptive mining function. It identifies relationships and the probability of their occurrence within a data set.
This huge amount of data needs to be used either for business growth or scientific discoveries. The process of discovering the patterns and relationships in data using the analysis tools is called Data Mining. The simplest form of data mining is as follows: 1. Describing
The SMAA-3 method applies ELECTRE III type pseudo-criteria in the analysis. The SMAA-O method is the extension of SMAA-2 for treating mixed ordinal and cardinal criteria. The SMAA-P method combines features from prospect theory and the SMAA method. The Ref-SMAA model rank the alternatives using reference points (Tervonen and Lahdelma,
Constructs are unobservable or latent factors represented by multiple variables. A latent construct is a hypothesized and unobserved concept that can be represented by observable variables. It is measured indirectly by examining consistency among multiple measured variables, also referred to as manifest variables or indicators. SEM’s foundation lies in two familiar multivariate techniques: factor analysis and multiple regression
Data can come in from many different sources and they can be structured, semi-structured, and even entirely unstructured data sources. It stimulates the generation of heterogeneous, high-dimensional, and nonlinear data with different representation forms, and just preparing it for analysis takes a significant amount of time and effort. However, for Industrial Big Data, there should be two more V’s. One is Visibility, which refers to the discovery of unexpected insights of the existing assets and/or processes and in this way transferring invisible knowledge to visible values. The other V is Value, which put an emphasis on the objective of Industrial Big Data analytics, creating values.
are the main challenges in Big Data. Data mining discover patterns from large data set. Data mining with Big data is a complex task. Here HACE theorem is proposed which finds Complex and Evolving relationships among data. It finds the characteristics
Among many testing activities, test case generation has become the most challenging and demanding task since it has a strong impact on the effectiveness and efficiency of whole testing process. REFERENCE ORCHESTRATED SURVEY Various techniques have been proposed for generating test data or test cases automatically like fuzzy logic, neural networks, GA, Genetic programming. A lot of work has been done using genetic algorithms for generation of automatic test cases apart from other techniques like structural and behavioural UML based testing, model based testing, structural testing using symbolic execution, random testing etc. These techniques directly or indirectly generate the test cases based on the specifications, source code and design
One of the analytical tool for analysis of data is data mining software. Using this software we can analyze data, classify them and summarize the relationships identified. Need of data mining in telecommunication In telecommunication industry, these are the reasons why data mining is used. • For detecting frauds Frauds are serious threats to the telecommunication industry, who will create lose of billions. • For retailing customers The study and research on customer database using data mining tools will help to know how to satisfy our customers.
Data Selection Once the data elements are chosen from several sources, it is essential to examine the value of the data. Data samples are accumulated from the sources and data profiling is carried out to recognize the issues of physical data quality. The data which are selected for an object are dependent on the patterns of significance. The data acquired from the sources will be required for three major purposes during the data mining process i.e. training the data mining model, testing it and applying it on the entire