Frequent Pattern Mining in Data Mining
Last Updated :
31 Aug, 2023
Frequent pattern mining in data mining is the process of identifying patterns or associations within a dataset that occur frequently. This is typically done by analyzing large datasets to find items or sets of items that appear together frequently.
Frequent pattern extraction is an essential mission in data mining that intends to uncover repetitive patterns or itemsets in a granted dataset. It encompasses recognizing collections of components that occur together frequently in a transactional or relational database. This procedure can offer valuable perceptions into the connections and affiliations among diverse components or features within the data.
Here’s an elaborate explanation of repeating arrangement prospecting:
- Transactional and Relational Databases:
Repeating arrangement prospecting can be applied to transactional databases, where each transaction consists of a collection of objects. For instance, in a retail dataset, each transaction may represent a customer’s purchase with objects like loaf, dairy, and ovals. It can also be used with relational databases, where data is organized into multiple related tables. In this case, repeating arrangements can represent connections among different attributes or columns.
- Support and Repeating Groupings:
The support of a grouping is defined as the proportion of transactions in the database that contain that particular grouping. It represents the frequency or occurrence of the grouping in the dataset. Repeating groupings are collections of objects whose support is above a specified minimum support threshold. These groupings are considered interesting and are the primary focus of repeating arrangement prospecting.
The Apriori algorithm is one of the most well-known and widely used algorithms for repeating arrangement prospecting. It uses a breadth-first search strategy to discover repeating groupings efficiently. The algorithm works in multiple iterations. It starts by finding repeating individual objects by scanning the database once and counting the occurrence of each object. It then generates candidate groupings of size 2 by combining the repeating groupings of size 1. The support of these candidate groupings is calculated by scanning the database again. The process continues iteratively, generating candidate groupings of size k and calculating their support until no more repeating groupings can be found.
During the Apriori algorithm’s execution, aid-based pruning is used to reduce the search space and enhance efficiency. If an itemset is found to be rare (i.e., its aid is below the minimum aid threshold), then all its supersets are also assured to be rare. Therefore, these supersets are trimmed from further consideration. This trimming step significantly decreases the number of potential item sets that need to be evaluated in subsequent iterations.
Frequent item sets can be further examined to discover association rules, which represent connections between different items. An association rule consists of an antecedent and a consequent (right-hand side), both of which are item sets. For instance, {milk, bread} => {eggs} is an association rule. Association rules are produced from frequent itemsets by considering different combinations of items and calculating measures such as aid, confidence, and lift. Aid measures the frequency of both the antecedent and the consequent appearing together, while confidence measures the conditional probability of the consequent given the antecedent. Lift indicates the strength of the association between the antecedent and the consequent, considering their individual aid.
Frequent pattern mining has various practical uses in different domains. Some examples include market basket analysis, customer behavior analysis, web mining, bioinformatics, and network traffic analysis. Market basket analysis involves analyzing customer purchase patterns to identify connections between items and enhance sales strategies. In bioinformatics, frequent pattern mining can be used to identify common patterns in DNA sequences, protein structures, or gene expressions, leading to insights in genetics and drug design. Web mining can employ frequent pattern mining to discover navigational patterns, user preferences, or collaborative filtering recommendations on the web.
Regular pattern extraction is a data extraction approach employed to spot repeating forms or itemsets in transactional or relational databases. It entails locating collections of objects that occur collectively often and possesses numerous uses in different fields. The Apriori algorithm is a well-liked technique utilized to effectively detect consistent itemsets, and association rule extraction can be carried out to obtain significant connections between objects.
There are several different algorithms used for frequent pattern mining, including:
- Apriori algorithm: This is one of the most commonly used algorithms for frequent pattern mining. It uses a “bottom-up” approach to identify frequent itemsets and then generates association rules from those itemsets.
- ECLAT algorithm: This algorithm uses a “depth-first search” approach to identify frequent itemsets. It is particularly efficient for datasets with a large number of items.
- FP-growth algorithm: This algorithm uses a “compression” technique to find frequent patterns efficiently. It is particularly efficient for datasets with a large number of transactions.
- Frequent pattern mining has many applications, such as Market Basket Analysis, Recommender Systems, Fraud Detection, and many more.
Advantages:
- It can find useful information which is not visible in simple data browsing
- It can find interesting association and correlation among data items
Disadvantages:
- It can generate a large number of patterns
- With high dimensionality, the number of patterns can be very large, making it difficult to interpret the results.
The increasing power of computer technology creates a large amount of data and storage. Databases are increasing rapidly and in this computerized world everything is shifting online and data is increasing as a new currency. Data comes in different shapes and sizes and is collected in different ways. By using data mining there are many benefits it helps us to improve the particular process and in some cases, it costs saving or revenue generation. Data mining is commonly used to search a large amount of data for patterns and trends, and not only for searching it uses the data for further processes and develops actionable processes.
Data mining is the process of converting raw data into suitable patterns based on trends.
Data mining has different types of patterns and frequent pattern mining is one of them. This concept was introduced for mining transaction databases. Frequent patterns are patterns(such as items, subsequences, or substructures) that appear frequently in the database. It is an analytical process that finds frequent patterns, associations, or causal structures from databases in various databases. This process aims to find the frequently occurring item in a transaction. By frequent patterns, we can identify strongly correlated items together and we can identify similar characteristics and associations among them. By doing frequent data mining we can go further for clustering and association.
Frequent pattern mining is a major concern it plays a major role in associations and correlations and disclose an intrinsic and important property of dataset.
Frequent data mining can be done by using association rules with particular algorithms eclat and apriori algorithms. Frequent pattern mining searches for recurring relationships in a data set. It also helps to find the inheritance regularities. to make fast processing software with a user interface and used for a long time without any error.
Association Rule Mining:
It is easy to find associations in frequent patterns:
- for each frequent pattern x for each subset y c x.
- calculate the support of y-> x – y.
if it is greater than the threshold, keep the rule. There are two algorithms that support this lattice
- Apriori algorithm
- eclat algorithm
| It performs “perfect” pruning of infrequent item sets. |
It reduces memory requirements and is faster. |
| It requires a lot of memory(all frequent item sets are represented) and support counting takes very long for large transactions. But this is not efficient in practice. |
Its storage of transaction list. |
The words support and confidence support the association rule.
- Support: how often a given rule in a database is mined? support the transaction contains x U y
- Confidence: the number of times the given rule in a practice is true. The conditional probability is a transaction having x as well as y.
working principle (it is a simple point of scale application for any supermarket which has a good off-product scale)
- the product data will be entered into the database.
- the taxes and commissions are entered.
- the product will be purchased and it will be sent to the bill counter.
- the bill calculating operator will check the product with the bar code machine it will check and match the product in the database and then it will show the information of the product.
- the bill will be paid by the customer and he will receive the products.
Tasks in the frequent pattern mining:
- Association
- Cluster analysis: frequent pattern-based clustering is well suited for high-dimensional data. by the extension of dimension the sub-space clustering occurs.
- Data warehouse: iceberg cube and cube gradient
- Broad applications
There are some to improve the efficiency of the tasks.
Closed Pattern:
A frequent pattern, it meets the minimum support criteria. All super patterns of a closed pattern are less frequent than the closed pattern.
Max Pattern:
It also meets the minimum support criteria(like a closed pattern). All super patterns of a max pattern are not frequent patterns. both patterns generate fewer numbers of patterns so therefore they increase the efficiency of the task.
Applications of Frequent Pattern Mining:
basket data analysis, cross-marketing, catalog design, sale campaign analysis, web log analysis, and DNA sequence analysis.
Issues of frequent pattern mining
- flexibility and reusability for creating frequent patterns
- most of the algorithms used for mining frequent item sets do not offer flexibility for reusing
- much research is needed to reduce the size of the derived patterns
- Frequent pattern mining has several applications in different areas, including:
- Market Basket Analysis: This is the process of analyzing customer purchasing patterns in order to identify items that are frequently bought together. This information can be used to optimize product placement, create targeted marketing campaigns, and make other business decisions.
- Recommender Systems: Frequent pattern mining can be used to identify patterns in user behavior and preferences in order to make personalized recommendations.
- Fraud Detection: Frequent pattern mining can be used to identify abnormal patterns of behavior that may indicate fraudulent activity.
- Network Intrusion Detection: Network administrators can use frequent pattern mining to detect patterns of network activity that may indicate a security threat.
- Medical Analysis: Frequent pattern mining can be used to identify patterns in medical data that may indicate a particular disease or condition.
- Text Mining: Frequent pattern mining can be used to identify patterns in text data, such as keywords or phrases that appear frequently together in a document.
- Web usage mining: Frequent pattern mining can be used to analyze patterns of user behavior on a website, such as which pages are visited most frequently or which links are clicked on most often.
- Gene Expression: Frequent pattern mining can be used to analyze patterns of gene expression in order to identify potential biomarkers for different diseases.
These are a few examples of the application of frequent pattern mining. The list is not exhaustive and the technique can be applied in many other areas, as well.
Conclusion:
It is impossible to give complete coverage of this topic with the limited space and our limited knowledge. Frequent pattern mining has achieved tremendous progress and claimed a good set of applications. However in-depth research is required that the field may have a long-lasting and deep impact on data mining applications.
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