What is knowledge flow in Weka?
What is knowledge flow in Weka?
Weka KnowledgeFlow. KnowledgeFlow. The KnowledgeFlow presents a “data-flow” inspired interface to Weka. The user can select Weka components from a tool bar, place them on a layout canvas and connect them together in order to form a “knowledge flow” for processing and analyzing data.
What is knowledge flow in data mining?
The Knowledge Flow interface is an alternative to the Explorer. You lay out filters, classifiers, evaluators, and visualizers interactively on a 2D canvas and connect them together with different kinds of connector. Data and classification models flow through the diagram!
What is weka experimenter?
The Weka Experimenter allows you to design your own experiments of running algorithms on datasets, run the experiments and analyze the results. It’s a powerful tool.
What is knowledge flow?
Knowledge flows refer to knowledge movements across people, organisations, places and time, depicting changes, shifts and applications.
What are the types of knowledge?
The 7 Types of Knowledge
- Explicit knowledge. Explicit knowledge can be documented, transmitted, and most importantly, learned by outsiders.
- Implicit knowledge.
- Tacit knowledge.
- Declarative knowledge.
- Procedural knowledge.
- A priori knowledge.
- A posteriori knowledge.
What is use training set in Weka?
Training data refers to the data used to “build the model”. For example, it you are using the algorithm J48 (a tree classifier) to classify instances, the training data will be used to generate the tree that will represent the “learned concept” that should be a generalization of the concept.
What is classification in Weka?
Advertisements. Many machine learning applications are classification related. For example, you may like to classify a tumor as malignant or benign. You may like to decide whether to play an outside game depending on the weather conditions.
What is clustering in weka?
A clustering algorithm finds groups of similar instances in the entire dataset. WEKA supports several clustering algorithms such as EM, FilteredClusterer, HierarchicalClusterer, SimpleKMeans and so on. You should understand these algorithms completely to fully exploit the WEKA capabilities.
What are the modes of knowledge management?
The four modes of knowledge conversion are: 1) socialization (from tacit knowledge to tacit knowledge); 2) externalization (from tacit knowledge to explicit knowledge); 3) combination (from explicit knowledge to explicit knowledge); and 4) internalization (from explicit knowledge to tacit knowledge).
What do you mean by Knowledge Management?
Knowledge management is the process by which an enterprise gathers, organizes, shares and analyzes its knowledge in a way that is easily accessible to employees. This knowledge includes technical resources, frequently asked questions, training documents and people skills.
