- The core purpose of Data Mining is to unearth important information in a dataset and make the best use of this to discover and decode future trends.
- Data Mining also incorporates data cleaning, pattern prediction, statistical analysis, data conversion, machine learning, and data visualization.
An association rule has 2 parts:
- an antecedent (if) and
- a consequent (then) An antecedent is something that’s found in data, and a consequent is an item that is found in combination with the antecedent. Have a look at this rule for instance:
“If a customer buys bread, he’s 70% likely of buying milk.”
Apriori algorithms: Prerequisite – Frequent Item set in Data set.
- All subsets of a frequent itemset must be frequent(Apriori propertry).
- If an itemset is infrequent, all its supersets will be infrequent.
I have implemented Apriori algorithm.
Clustering is the process of making a group of abstract objects into classes of similar objects.
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Requirements of Clustering in Data Mining
- Scalability
- Ability to deal with different kinds of attributes
- Discovery of clusters with attribute shape
- High dimensionality
- Ability to deal with noisy data
- Interpretability
I have implemented K-means cluster with initial seed selection and K-medoids clustering algorithm.