Classification Using Frequent Patterns in Data Mining

A data mining approach called frequent pattern mining is used to find recurring patterns in a dataset. It is a kind of unsupervised machine-learning technique that looks for and identifies patterns in data using algorithms. This method can be applied to find products that are frequently purchased together or to find products that are more likely to be purchased by particular demographic groups. Numerous applications of this method include client segmentation, fraud detection, and marketing analysis. Frequent pattern mining can be utilized in classification tasks to identify the patterns that are most likely related to a particular class.

Frequent patterns refer to item sets, subsequences, or substructures that appear frequently in a data set.

It works by scanning a data collection for common patterns, or item sets, and then utilizing those patterns to categorize previously undiscovered data items. Once learnt, the patterns may be utilized to categorize previously unknown data items, such as new consumer purchases or new customer behaviours. This categorization may be used for a number of purposes, including forecasting customer turnover and detecting fraudulent activity.

Examples of classification in real life:

1. Assume you have a dataset that contains information about consumer transactions at a shop. You might detect patterns of things that are frequently purchased together using frequent pattern mining. These patterns might then be used to train a classifier to anticipate which things a consumer is likely to buy based on recent purchases.

Explanation:

Frequent pattern mining can be used to find patterns of products that are commonly bought together in a consumer transaction dataset. For instance, frequent pattern mining can be used to find pairs of things (like “milk and bread”) or bigger groups of items (like “milk, bread, and eggs”) that are frequently bought together if the dataset has information on which items were purchased in each transaction. Once these patterns have been discovered, a classifier can be trained using them. A classifier is a machine learning model that has been trained to predict a specific outcome, in this case, the products that a customer is most likely to purchase based on previous purchases. The classifier may learn which things are likely to be bought together by being trained on the patterns discovered through frequent pattern mining.

2. A hospital is thinking about employing a new nurse. They are searching for someone with extensive patient care expertise.

Explanation:

The hospital would use the information on the new nurse’s credentials, experience, and prior performance to train a model that determines whether or not they have “vast patient care knowledge.” To train the model, the hospital would use a labelled dataset of previously employed nurses. The classifier’s predictions would be relied upon to determine whether or not to hire the new nurse. To choose the finest applicant, additional criteria including background checks, reference checks, and psychological assessments may also be taken into consideration. The hospital would next determine whether or not to hire the new nurse based on the classifier’s predictions. The hospital would probably be more likely to hire the new nurse if the classifier forecasts that she has “significant patient care knowledge.”

3. A corporation is in the market for a new accountant. They seek someone who can grasp financial reports fast and properly.

Explanation:

The company would use the information on the new accountant’s training, experience, and historical performance to train a model that determines whether or not they can “understand financial reports quickly and correctly.” The business would train the model using a labelled dataset of previous accountants. Whether or not to hire the new accountant would be determined by the classifier’s predictions. To choose the finest applicant, additional criteria including background checks, reference checks, and psychological assessments may also be taken into consideration.

4. A supermarket is seeking a new cashier. They want someone who can handle a tremendous workload while remaining cool under pressure.

Explanation:

The store would use the information on the new cashier’s credentials, experience, and prior performance to develop a model in classification data mining that determines whether or not they can “manage a massive workload while keeping cool under pressure.” The store would train the model using a labelled dataset of previous cashiers. Whether or not to hire the new cashier would be determined by the classifier’s predictions. To choose the finest applicant, additional criteria including background checks, reference checks, and psychological assessments may also be taken into consideration.

5. A corporation is in the market for a new marketing director. They want someone who can design efficient marketing initiatives.

Explanation:

The company would use the information on the incoming marketing director’s credentials, expertise, and historical performance to train a model that determines whether or not they can “create efficient marketing efforts.” The business would train the model using a labelled dataset of previous marketing directors. The classifier’s predictions would be used to determine whether or not to hire the new marketing director. To choose the finest applicant, additional criteria including background checks, reference checks, and psychological assessments may also be taken into consideration.

There are a number of algorithms that can be used for classification using frequent pattern mining. Some examples include:

1. Apriori Algorithm:

The Apriori algorithm is an algorithm for finding frequent item sets in a given dataset. It is an unsupervised learning technique that employs a “bottom-up” strategy to discover frequent itemsets in a dataset by first recognizing individual items in the dataset and then looking for combinations of items that appear often together. The Apriori technique may be used to identify the rules that govern the relationships between various objects in a collection. It is frequently used in market basket analysis, which seeks to find goods that are frequently purchased together.

2. FP-Growth Algorithm:

The FP-Growth (Common Pattern Growth) algorithm is a data mining technique that finds frequent patterns or itemsets in a dataset. It operates by building an FP-Tree, which is a compact representation of the dataset. The FP-Tree is then utilized to construct common patterns from the ground up. The FP-Growth technique is very scalable and can effectively detect common patterns in huge datasets. It is also more efficient than another common approach for mining frequent item sets, the Apriori algorithm.

3. Closed Frequent Itemset Mining:

Closed frequent itemset mining is a kind of frequent itemset mining in which all itemsets in a given dataset with a frequency that meets or exceeds a predetermined threshold are discovered. The technique works by first generating a list of all frequent item sets in the dataset, then iteratively evaluating each item set to determine whether any supersets of the itemset have a frequency that meets or exceeds the stated threshold. Any supersets that meet the criteria are added to the list of frequently occurring itemsets. This procedure is continued until no further supersets are discovered.

4. Naive Bayesian Algorithm:

Naive Bayes is a form of supervised machine learning method that is used for classification and is based on Bayes’ Theorem. It is a probabilistic method that predicts using the probability of each attribute belonging to each class. The Naive Bayes method is based on the assumption that all qualities are independent of one another. This streamlines the computation of probabilities, allowing the algorithm to easily forecast a class of a new data point.

Application of Classification using frequent patterns:

1. Transactional database:

• Relational dataset, Customer transaction data, etc.
• Recurring itemset.

2. Sequence database:

• Web log data.
• Frequent sequential patterns.

3. Graph database:

• Frequent substructures.

Conclusion:

Frequent pattern mining is a strong tool for classifying data. It can aid in the identification of data patterns that can be utilized to draw conclusions and make predictions.

Identifying these patterns can assist in making better-informed decisions in a range of scenarios, including marketing, fraud detection, and consumer segmentation.

Furthermore, frequent pattern mining can reveal previously unknown correlations between variables. This can lead to more precise and nuanced forecasts. Finally, frequent pattern mining may be a useful technique for analysing data and making judgements.