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TECHNIQUES IN DATA MINING, : - Coggle Diagram
TECHNIQUES IN DATA MINING
Clustering
What is
clustering
?
Clustering is an unsupervised machine learning approach for discovering and grouping related data points in huge datasets without regard for the outcome. Clustering is commonly used to organise data into structures that are easier to understand and manipulate. It's important to remember that, while clustering is a common method, it's not a unified concept, as there are several algorithms that employ cluster analysis with distinct principles.
When to use
clustering
?
When starting from a large, unstructured dataset
When don’t know how many or which classes your data is divided into
When manually dividing and annotating data is too resource-intensive
When looking for anomalies in your data
Where to use
clustering
?
Market research and customer segmentation
Biological data and medical imaging
Search result clustering
Recommendation engine
Pattern recognition
Social network analysis
Image processing
How to use
clustering
?
Select K, the number of clusters you want to identify. Let’s select K=3.
Randomly generate K (three) new points on your chart. These will be the centroids of the initial clusters.
Measure the distance between each data point and each centroid and assign each data point to its closest centroid and the corresponding cluster.
Recalculate the midpoint (centroid) of each cluster.
Repeat steps three and four to reassign data points to clusters based on the new centroid locations. Stop when either:
a. The centroids have been stabilized; after computing the centroid of a cluster, no data points are reassigned.
b. The predefined maximum number of iterations has been achieved.
Decision Tree
What is
decision tree
?
A decision tree is a structure composed of a root node, branches, and leaf nodes. Each internal node represents a test on an attribute, each branch represents the result of a test, and each leaf node represents a class label. The root node is the tree's highest node.
When to use
decision tree
?
Assessing prospective growth opportunities
Using demographic data to find prospective clients
Serving as a support tool in several fields
How to use
decision tree
?
Start with your overarching objective/ “big decision” at the top (root)
Draw arrow lines for every possible course of action, stemming from the root. Include any costs associated with each action, as well as the likelihood for success.
Attach leaf nodes at the end of your branches
Determine the odds of success of each decision point
Evaluate risk vs reward
Where to use
decision tree
?
Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal
Evaluation Method
What is
evaluation method
?
Evaluation is a collection of research methods and associated approaches that serve a certain objective. They allow for the evaluation of actions and activities in terms of values, criteria, and standards. At the same time, assessment is a method that tries to improve public sector and policymaking effectiveness.
When to use
evaluation method
?
Evaluation should happen at regular intervals in the life of a project, to build up a clear picture of progress and impact. The type of programme will affect timing. For example, if objectives are long-term it may be two years before any useful assessment of progress can be made.
How to use
evaluation method
?
Test
Pre and Post- Test
Test against control group
Participation
Attandance
Completion
Data Collection
Surveys
Interviews
Where to use
evaluation method
?
Evaluation method can be used everywhere and anywhere and in any situation if you want because this method is a universal method in machine learning
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