An Efficient Incremental Mining Algorithm for Dynamic Databases

HEPM: High-efficiency pattern mining

Discovering interesting patterns in transactional databases is often a challenging area by the length of patterns and number of transactions in data mining, which is prohibitively expensive in both time and space. Closed itemset mining is introduced from traditional frequent pattern mining and having its own importance in data mining applications. Recently, regular itemset mining gained lot of attention in data mining research because of its occurrence behavior. In this paper we propose a new method called RCP-method (regular-closed pattern method) to mine regular-closed itemsets in transactional database by using vertical data format. Our RCP-method generates complete set of regular-closed patterns in transactional databases for a user given regularity threshold and support. Our experimental results show that this method is efficient in memory and execution time.

Analysis of Transaction Database Using Apriori Algorithm. This study examines the use of the Apriori algorithm for analysing transaction databases. The Apriori algorithm is a fundamental technique in data mining that allows for the efficient discovery of frequent patterns and association rules in large datasets. The Apriori algorithm employs a two-step approach. Initially, it identifies frequent items in the database based on a user-defined minimum support threshold. Subsequently, it generates association rules that describe relationships between these frequent items based on metrics such as confidence and lift. This paper provides an in-depth explanation of the Apriori algorithm, emphasizing its strengths and limitations. Additionally, it presents various applications of the Apriori algorithm in real-world scenarios, such as shopping cart analysis, cross-selling and upselling, and customer segmentation. The significance of this study lies in its comprehensive analysis of the Apriori algorithm and its practical relevance in diverse data mining tasks. It serves as a valuable resource for researchers, practitioners, and anyone seeking to understand the fundamentals and applications of association rule mining. Keywords: Datamining, OLTP, Apriori algorithm, Item sets and Market Basket Analysis

Computers have significantly impacted various fields, but managing vast amounts of information remains challenging. Artificial intelligence helps machines make data-driven decisions, but large datasets still pose difficulties for researchers. To tackle challenges and gain deep insights from large datasets, we proposed a novel technique using Geometric Progression series numbers (GPLN) for labeling singleton frequent itemsets and Cumulative Geometric Progression series numbers (CGPLN) for labeling itemsets with multiple frequent items. Initially, the algorithm used 2 as the constant 'r' for generating the series, but that was inadequate for large datasets. So, this paper proposes the Jagged Itemset Counting (JIC) algorithms 1 and 2 by reducing the value of ‘r’ and introduced dotted pairs for CGPLN labels to represent frequent itemsets. This redefined methodology requiring two passes over the transaction database: the first pass involves pre-processing, identifying singleton (1-k) frequent itemsets, determining GPLN, and partitioning the dataset. The partitions are processed sequentially: JIC-Algorithm-1 is applied to the first partition and JIC-Algorithm-2 to the remaining partitions. The n-k frequent itemsets from all partitions are combined using the Join algorithm, alongside 1-k itemsets found earlier. For small and medium-sized data, the JIC methodology outperforms Apriori and Eclat algorithms, showing better execution time even at low support thresholds. In Big Data scenarios, while FP-Growth and Eclat struggled, the proposed methodology excelled in execution time, main memory consumption, and disk memory utilization.

Pattern mining refers to a subfield of data mining that uncovers interesting, unexpected, and useful patterns from transaction databases. Such patterns reflect frequent and infrequent patterns. An abundant literature has dedicated in frequent pattern mining and tremendous efficient algorithms for frequent itemset mining in the transaction database. Nonetheless, the infrequent pattern mining has emerged to be an interesting issue in discovering patterns that rarely occur in the transaction database. More researchers reckon that rare pattern occurrences may offer valuable information in knowledge data discovery process. The R-Eclat is a novel algorithm that determines infrequent patterns in the transaction database. The multiple variants in the R-Eclat algorithm generate varied performances in infrequent mining patterns. This paper proposes IF-Postdiffset as a new variant in R-Eclat algorithm. This paper also highlights the performance of infrequent mining pattern from the transaction database among different variants of the R-Eclat algorithm regarding its execution time.

Under the environment of big data, efficiency is low and there are many candidates when the traditional serial Apriori algorithm in dealing with massive data. This paper proposes a parallel better algorithm based on MapReduce distributed architecture. Based on the basic Apriori algorithm on MapReduce, this paper makes a reconstruction of the original transaction database, and implements parallel in data set fragmentation. The algorithm optimizes the transaction database, candidate item sets counting and pruning strategy. The experimental results show that the improved algorithm proposed in this paper can reduce the candidate items and improve the efficiency.

Incremental algorithms can manipulate the results of earlier mining to derive the final mining output in various businesses. This study proposes a new algorithm, called the New Fast UPdate algorithm (NFUP) for efficiently incrementally mining association rules from a large transaction database. NFUP is a backward method that only requires scanning incremental database. Rather than rescanning the original database for some new generated frequent itemsets in the incremental database, we accumulate the occurrence counts of newly generated frequent itemsets and delete infrequent itemsets obviously. Thus, NFUP need not rescan the original database and to discover newly generated frequent itemsets. NFUP has good scalability in our simulation.

A provided data from a transactional database, has supported technical development which can automatically find product association or items saved in the database. This finding association rules between saved product in database known as mining association rules. There are so many theories and algorithm developed for conducting mining association rules. One of algorithm developed is Apriori algorithm. This method, has a main goal to find the maximum frequent itemset. Next, this frequent itemset will be generated into associative rules which are not shown before in the database, become valuable information for considering materials in the decision process. Apriori algorithm is a interpretation technique of mining association rules, will be implemented into a web based software. On the software test which use in some different data, it’s concluded that time for mining association rules depends on the presence of every item in every transaction, total of transaction, minimum support and minimum confidence. For smaller value of minimum support and minimum confidence that entered, program will generate more association rules, vice versa.

Nowadays due to the rapid advances in the field of information systems, transactional databases are being updated regularly and/or periodically. The knowledge discovered from these databases has to be maintained, and an incremental updating technique needs to be developed for maintaining the discovered association rules from these databases. The concept of Temporal Association Rules has been introduced to solve the problem of handling time series by including time expressions into association rules. In this paper we introduce a novel algorithm for Incremental Mining of General Temporal Association Rules (IMTAR) using an extended TFP-tree. The main benefits introduced by our algorithm are that it offers significant advantages in terms of storage and running time and it can handle the problem of mining general temporal association rules in incremental databases by building TFP-trees incrementally. It can be utilized and applied to real life application domains. We demonstrate our algorithm and its advantages in this paper.

High-utility itemset mining (HUIM) is creating new heights of challenge in the areas of research in big data analytics blending data structures, AI, and machine learning techniques to find efficient utility patterns. In the recent past, the merchandise market researchers were pretty much interested in analyzing the purchasing patterns and forecast new profitable area of business. HUIM is that one kind where we get business intelligence and has become the most basic method of finding the knowledge in decision making and optimizing the decisions of market analysis, streaming analysis, biomedicine, mobile computing, stock exchanges, etc. HUIM was initially applied widely in the transactional databases, later applied recursively in incremental databases, further moved to dynamic databases like temporal, spatial, and data stream databases. This chapter highlights the insights of various existing algorithms present in the literature based on several applications and tries to update the various availabilities of different approaches for coining latest research in this domain.


 
 
 
 Apriori algorithm is a classical algorithm of association rule mining and widely used for generating frequent item sets. However, the original Apriori algorithm has some limitation such as it needs to scan the dataset many times to discover all frequent itemset and generate huge number of candidate itemset. To overcome these limitations, researchers have made a lot of improvements to the Apriori such as candidate generation, without candidate generation, transaction reduction, partitioning, and sampling. When it comes to mine massive data, these algorithms failed to prove efficiency because limitation of the processing capacity, storage capacity, and main memory constraints. Therefore, parallel and distributed algorithms are developed to perform large-scale computing in ARM on multiple processors. However, the problems with most of the parallel and distributed framework are overheads of managing distributed system, lack of high level parallel programming language, and node failures. Hadoop-MapReduce is an efficient, scalable, and simplified programming model for massive data processing and it also available on cloud environment. Cloud computing offers huge computing resources, and capacities to solve big data challenges. Recently many parallel algorithms have been proposed on Hadoop-MapReduce to enhance the performance of Apriori algorithm but there are some drawbacks: since multiple scan over the dataset is needed to generate candidate itemset, it consume more execution time. The aim of this study is to propose a parallel Transaction Reduction MapReduce Apriori algorithm (TRMR-Apriori) which is reduce unnecessary transaction values and transactions from the dataset in parallel manner to overcome above problems. The experiments show that TRMR-Apriori is able to achieve better execution time to discover frequent itemset those of previous sequential ARM algorithms such as Apriori, AprioriTid, Eclat, and FP-Growth and the previous parallel algorithms such as PApriori, MRApriori, and Modified Apriori with different condition on homogeneous computing environment using Hadoop-MapReduce platform in cloud. Overall, the TRMR-Apriori shows the strength to extract the frequent itemset from massive dataset in cloud.
 
 
 
 

In view of the existing defects, based on the analysis of the ideas and methods improved by predecessors, and combining with the characteristics of mobile platform teaching evaluation, the original classic Apriori algorithm is improved. The improved database only needs to scan the original database once, store the transactions belonging to each frequent itemset, and store these collections in the newly added database. Therefore, when counting the candidate item set generated by self-join, only the intersection of the transaction set stored in the subset of the candidate item set is required to obtain the number of supported items in the candidate item set. The improved algorithm can reduce the scanning times of the original database and compress the scanning range, thus improving the efficiency of the algorithm. The teaching evaluation system based on mobile platform meets the needs of all the teachers and students and provides great convenience. Therefore, it can greatly improve the participation rate and make the data more comprehensive and complete.

Frequent Itemset Mining FIM is one of the most investigated fields of data mining. The goal of Frequent Itemset Mining FIM is to find the most frequently-occurring subsets from the transactions within a database. Many methods have been proposed to solve this problem, and the Apriori algorithm is one of the best known methods for frequent Itemset mining FIM in a transactional database. In this paper, a parallel Frequent Itemset Mining Algorithm, called Accelerating Parallel Frequent Itemset Mining on Graphic Processors with Sorting APFMS, is presented. This algorithm utilizes new-generation graphic processing units GPUs to accelerate the mining process. In it, massive processing units of GPU were used to speed up the frequent item verification procedure on the OpenCL platform. The experimental results demonstrated that the proposed algorithm had dramatically reduced computation time compared with previous methods.

The transaction database contains a very large and irregular dataset that requires another mechanism to read it, even though there is a lot of new knowledge that can be revealed, including associations or relationships between goods or products that are often purchased by customers. The new finding of the relationship between these variables is usually called association rule mining. The algorithm that is developing and often used is frequent pattern-growth (FP-Growth). The problem of very many transaction databases also occurred in Mr. A. So, in this research, we will look for customer patterns using the FP-Growth algorithm. The algorithm aims to find the maximum frequent itemset. The frequent itemset will be generated into associative rules so that it becomes valuable new knowledge. This knowledge can be used as a reference and consideration in making decisions. The FP-Growth algorithm will be implemented using the rapidminer tools on the transaction data of Mr.A's goods sales. The pattern of rules that will be searched for is based on data on sales of goods transactions. The results of the study obtained six association rules with five conclusions being the gift category. So that the suggestion for decision making is to lay out items close to and around the gift category in order to improve marketing and service strategies in order to attract the attention and interest of pointers in making purchases of goods.

Tourist attractions are places of interest and visited by tourists because they have certain appeals. These attractions can include natural tourism, cultural tourism, artificial tourism, or agro-tourism. Simalungun Regency offers various interesting tourist spots, ranging from natural beauty such as waterfalls and lakes to rich traditional cultures that reflect local history and customs. However, information about tourist visit patterns remains limited. A deeper understanding of these patterns is crucial, especially for tourism managers in designing effective and sustainable development strategies. Therefore, this study aims to identify associations or relationships between tourist attractions based on tourist visit data. The Apriori algorithm was chosen for its ability to discover high-frequency patterns in large datasets. These patterns are expected to provide valuable insights for tourism managers in designing effective promotional strategies. Apriori is a well-known algorithm in data mining, particularly for finding frequent itemsets using association rule techniques. The algorithm is carried out by determining frequent itemsets, from 1-itemset to multi-itemsets, until the association rules are derived from the pre-selected data. To obtain these frequent itemsets, each selected data must meet the minimum support and minimum confidence requirements so that the resulting association rules are relevant and practically applicable.