Computational Thinking for Governance Analytics 5th Session

1. Clustering

1.1. concept->

1.1.1. organize rows

1.2. problem

1.2.1. compute similarity among cases

1.3. goal

1.3.1. cases into groups or conglomerates

1.4. strategies

1.4.1. hard

1.4.1.1. hierarchical approach

1.4.1.1.1. agglomerative

1.4.1.1.2. divisive

1.4.2. soft

1.5. challenges

1.5.1. valid computation

1.5.1.1. amount of clusters

1.5.1.2. belonging to a cluster

1.5.2. interpretation for decision making

2. Factor Analysis

2.1. concept->

2.1.1. organize columns

2.2. problem

2.2.1. variables into smaller set of variables (factors)

2.3. goal

2.3.1. identify latent (unmesured) concept from set of measured variables

2.4. strategies

2.4.1. exploratory

2.4.2. confirmatory

2.5. challenges

2.5.1. valid computation

2.5.1.1. enough cases (sample size)

2.5.1.2. adequate correlation matrix

2.5.2. interpretation for decision making