[Paper Review] Foundation for Frequent Pattern Mining Algorithms Implementation

Tanna Paresh, Yogesh Ghodasara|arXiv (Cornell University)|Feb 8, 2014

Data Mining Algorithms and Applications5 references4 citations

TL;DR

This paper establishes a foundational framework for implementing frequent pattern mining algorithms by analyzing and comparing Apriori and Direct Hashing and Pruning (DHP) techniques. It evaluates their performance in candidate generation, database scanning, and transaction pruning, providing a comparative basis for developing more efficient algorithms in association rule mining.

ABSTRACT

As with the development of the IT technologies, the amount of accumulated data is also increasing. Thus the role of data mining comes into picture. Association rule mining becomes one of the significant responsibilities of descriptive technique which can be defined as discovering meaningful patterns from large collection of data. The frequent pattern mining algorithms determine the frequent patterns from a database. Mining frequent itemset is very fundamental part of association rule mining. Many algorithms have been proposed from last many decades including majors are Apriori, Direct Hashing and Pruning, FP-Growth, ECLAT etc. The aim of this study is to analyze the existing techniques for mining frequent patterns and evaluate the performance of them by comparing Apriori and DHP algorithms in terms of candidate generation, database and transaction pruning. This creates a foundation to develop newer algorithm for frequent pattern mining.

Motivation & Objective

• To analyze existing frequent pattern mining techniques, particularly Apriori and DHP, for their efficiency in handling large-scale data.
• To evaluate the performance of Apriori and DHP algorithms in terms of candidate generation, database scanning, and transaction pruning.
• To establish a comparative foundation for designing more efficient frequent pattern mining algorithms.
• To support the development of next-generation algorithms by identifying strengths and limitations of current approaches.

Proposed method

• The study conducts a comparative analysis of Apriori and DHP algorithms using standard performance metrics in frequent pattern mining.
• It evaluates candidate generation processes, focusing on the number of candidate sets produced and the computational cost.
• Database and transaction pruning techniques are assessed for their effectiveness in reducing search space and improving efficiency.
• The analysis is based on theoretical and empirical evaluation of algorithmic behavior on transactional databases.
• Performance comparison is conducted using metrics such as execution time, number of database scans, and candidate set size.

Experimental results

Research questions

• RQ1 How do Apriori and DHP algorithms compare in terms of candidate generation overhead?
• RQ2 What is the impact of database and transaction pruning on the efficiency of frequent pattern mining?
• RQ3 Which algorithm—Apriori or DHP—exhibits better performance in reducing the number of database scans?
• RQ4 How do the computational costs of candidate generation and pruning vary between the two algorithms?

Key findings

• The DHP algorithm outperforms Apriori in reducing the number of candidate sets generated, leading to improved efficiency.
• DHP demonstrates superior performance in minimizing database scans due to its hash-based candidate pruning mechanism.
• Transaction pruning in DHP is more effective than in Apriori, reducing the search space more efficiently.
• The study confirms that DHP reduces computational overhead in candidate generation compared to Apriori's level-wise approach.