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What is Constraint-Based Frequent Pattern Mining?


In order to avoid independent or tedious rules, users often want to guide data mining toward  interesting patterns that align with their desired form. Constraint-based frequent mining in data mining is an approach that filters results by using predefined constraints to identify frequent patterns in large datasets. Algorithms like Apriori, FP-Growth, and Eclat are used to discover significant patterns meeting constraints such as minimum support or confidence thresholds.This helps efficiently comprehend complex data, leading to informed marketing, healthcare, and financial decision-making. Understanding constraint-based frequent pattern in data mining begins with understanding data science; you can get an insight into the same through our Data Science Training.   

Constraint-Based  Frequent Pattern Mining

Data mining allows for extracting  thousands of rules that appear to be important from a dataset; nevertheless, likely,  the majority of these rules will not provide customers with any value. Users often have a clear concept of the "form" of the patterns or rules they want to discover and the "direction" of mining that may lead to intriguing pattern discoveries. It's also possible that they have a preconceived understanding of what the "conditions" of the rules are, which would prohibit them from seeking rules that they already know are irrelevant to the situation. As a result of this, a useful heuristic is to have users pick constraints based on their own intuition or preconceptions about what should be allowed. This approach is referred to as "constraint-based mining," which is an industry term. The following are some illustrative examples of potential restrictions:

  • Knowledge Type of Constraint: These characteristics, which may include association, correlation, categorization, or grouping, describe the nature of the knowledge that is to be mined. 
  • Data Constraint: These characteristics are used to determine the information that is required to finish a job. These constraint attempts can be guided in the right direction by imposing constraints, such as limitations on the dimensions or layers of the data, abstractions, or thought hierarchies.
  • Interestingness Constraints: Limitations on interestingness are utilized  in the process of establishing minimum and maximum values for statistica measures of rule interestingness, including support, confidence, and correlation. Limitations are placed on the interestingness of rules.
  • Rule Constraint: The form or needs of the rules are outlined by the constraints that are placed on the rules to be mined.

In the form of metarules, one can express limitations placed on the number of predicates permitted to occur in the antecedent or consequent of a rule, as well as the relationships between attributes, attribute values, and/or aggregates (rule templates).It is necessary to have both a graphical user interface and a high-level declarative data mining query language to  be able to express such constraints.

The first four kinds of limitations have each received a substantial amount of attention over the entirety of this book and in the chapters that came before this one. In this section, we will discuss how the application of rule limits may assist in reducing the overall scope of the mining process. This constraint-based mining approach optimizes  the data mining process by allowing users to describe the rules they want to uncover and then look  for those rules. In addition, the user-specified limits can be utilized   by an intelligent mining query optimizer, which in turn increases the mining operation's efficiency.

When using constraints, it's possible to do interactive exploratory mining and analysis. In this course, you'll learn about metarule-guided mining, a technique in which syntactic rule restrictions are described using rule templates. details the use of data space pruning (removing portions of the data space for which further exploration cannot contribute to finding patterns matching the requirements) and pattern space pruning (removing portions of the pattern space that are not being mined).

We present anti monotonicity , monotonicity, and succinctness as classes of traits that aid in pruning pattern spaces via constraint-based search space reduction. Convertible constraints are discussed; they are a subset of monotonic and anti-monotonic  constraints that may be pushed farther into the iterative mining process without losing their pruning power with the right data ordering.We investigate how data space pruning might be included in  a data mining workflow by introducing two sets of properties: data succinctness and data anti monotonicity .

We will assume the user is looking for association rules for the sake of discussion. Adding a correlation measure of interestingness to the support-confidence framework makes it simple to apply the proposed methods to mining correlation rules. For a better understanding of constraint-based frequent pattern mining, you need to learn about the six stages of data science processing. 

Metarule-Guided Mining of Association Rules

The user can specify the syntactic form of rules that the user is interested in mining when using a metarule. The mining process's efficiency may be helped  by utilizing  the rule forms as limitations, which can be done. Metarules can be developed manually by the analyst based on their prior knowledge, expectations, or intuition in relation to the data, or they can be generated automatically depending on the schema of the database.

EXAMPLE : Extracting with a Meteorite as a Guide

Assume you are a market analyst for AllElectronics and have access to both a list of customer transactions and demographic information about the company's clientele (such as age, residence, and credit score). You want to learn if there is a correlation between certain consumer characteristics and the products they buy. You want to know which combinations of client characteristics boost the sale of office software rather than discovering all the association rules expressing these associations. You may use a metarule to define the type of rules you want to uncover.

Metarules like P1(X, Y) ∧ P2(X, W) ⇒ buys(X, “office software”) are examples of such expressions, where P1 and P2 are predicate variables that are instantiated to attributes from the given database during mining, X is a variable representing a customer, and Y and W take on values of the attributes assigned to P1 and P2 during the process. To examine P1 and P2 instantiations, a user will often provide a set of properties. Otherwise, a predetermined set may be applied.

In most cases, a metarule usually generates a working hypothesis about the relationships the user wishes to test or verify. As a result, the data mining software may look for a set of guidelines that correspond  to the provided metarule. For example, the Rule is in agreement with the Metarule expression:

age(X, “30..39”) ∧ income(X, “41K..60K”)⇒buys(X, “office software”).

Let's pretend we're interested in mining association rules across dimensions, like in-

Example 1. Specifically, a Metarule is a Rule Template with The Form P1 ∧ P2 ∧ ··· ∧ Pl ⇒ Q1 ∧ Q2 ∧ ··· ∧ Qr,

Where Pi (i = 1,..., l) and Qj (j = 1,..., rare instantiated predicates or predicate variables. Let p = l + r represent the total number of predicates in the metarule. Finding all common p-predicate sets, Lp, is necessary for discovering inter-dimensional association rules that fit the template.

In order to calculate the confidence of rules derived from Lp, we additionally need the support or count of the l-predicate subsets of Lp.

In this situation, multidimensional association rule mining is often applied. It is possible to construct effective strategies for metarule-guided mining by extending these approaches with the constraint-pushing techniques discussed below.

1) Reducing The Size of The Pattern Space and The Data Space in Order to Generate Constraint-Based Patterns

Rule constraints can take many different forms. Some examples of rule constraints are the projected set/subset connections of the variables in the mining rules, constant initiation of variables, and constraints on aggregate functions. Users frequently depend on their expertise with the program  or data to  define rule constraints for the mining work. This is done in order to prevent the mining task from becoming too complex. You can use a more conventional mining technique known as a metarule, or you may use these rule constraints instead of a metarule. In this piece, we take a look at the possible benefits in productivity that might result from incorporating rule limits into your mining operation. To begin, we will  look at a case study of hybrid-dimensional association rule mining, which employs rule limitations.

During the mining process, an effective frequent pattern mining processor can narrow its focus by eliminating irrelevant patterns or discarding irrelevant data. The former determines whether or not a pattern may be trimmed by analyzing  potential patterns. The Apriori attribute is used to eliminate a pattern if it cannot be further refined by mining into a super pattern . The latter examines the dataset to see if the specific data item may help generate satisfiable patterns (for a certain pattern) in the remaining mining phase. In that case, the information is omitted from further analysis. The term "pattern pruning constraint" refers to a constraint that can be used for pruning in the pattern space. In contrast, "data pruning constraint" refers  to a constraint that can be used for pruning in the data space.

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2) Pattern Space Pruning Through The Use of Pattern Pruning Constraints

There are five kinds of pattern mining restrictions, and each one is based on how a constraint may interact with the process of pattern mining. 

  1.  Antimonotonic 
  2.  Monotonic
  3.  Concise
  4.  Convertible
  5.  Inconvertible
3) Data Pruning: Reducing Data Volume Constraints for Pruning

In constraint-based frequent pattern mining, pruning data space is the second method of search space reduction. If a piece of data is not going to help generate satisfactory  patterns in the mining process, it is removed. Shortness and non-monotonicity of data are two characteristics we take into account.

When employed at the outset of a pattern mining process to prune the data subsets that do not fulfill  the constraints, constraints are considered data-succinct. Suppose a mining query specifies that the mined pattern must include the word "digital camera," for instance. In that case, any transactions that do not include "digital camera" may be removed from the dataset before mining even begins.

Intriguingly, many constraints are data anti-monotonic, meaning that they allow for the elimination of data entries that fail to conform to the present pattern during mining. Due to the fact that it cannot aid in the creation of a super pattern  of the present pattern throughout the remaining mining operation, we prune it.

Advantages of Constraint-Based Mining

There are several benefits associated with using constraint-based methods for frequent pattern discovery:

  • Increased Efficiency - By incorporating user-defined constraints into the algorithm design process, it's possible to reduce search space significantly.
  • Improved Accuracy - Constraints help eliminate irrelevant or redundant results leading to more accurate findings.
  • Flexibility - Users can customize their analysis according to their needs by defining different types of constraints.
  • Better Interpretability - The use of additional information through constraints helps make discovered patterns more interpretable and useful than those obtained using only statistical measures like support count or confidence level.


Data mining will become more difficult as we enter the digital era. In this article, we have provided valuable insights into what constitutes constraint-based frequent pattern mining (CBFPM). It differs from traditional approaches, and CBFPM offers significant advantages, including increased efficiency, accuracy, flexibility, and better interpretability, among others. We have also provided various algorithms today, such as the apriori-close algorithm (ACA), the FP-growth algorithm, and the Eclat algorithm. It provides readers with a summary of what makes each unique and suitable. Hence, it becomes easy for them to select the best tools and methods to achieve their goals and results.You can also learn about neural network guides and python for data science if you are interested in further career prospects of data science.

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