A study on case-based reasoning using generalized association rules mining

Multi-dimensional semantic clustering of large databases for association rule mining

Association rule mining (ARM) is a commonly encountred data mining method. There are many approaches to mining frequent rules and patterns from a database and one among them is heuristics. Many heuristic approaches have been proposed but, to the best of our knowledge, there is no comprehensive literature review on such approaches, yet with only a limited attempt. This gap needs to be filled. This paper reviews heuristic approaches to ARM and points out their most significant strengths and weaknesses. We propose eight performance metrics, such as execution time, memory consumption, completeness, and interestingness, we compare approaches against these performance metrics and discuss our findings. For instance, comparison results indicate that SRmining, PMES, Ant‐ARM, and MDS‐H are the fastest heuristic ARM algorithms. HSBO‐TS is the most complete one, while SRmining and ACS require only one database scan. In addition, we propose a parameter, named GT‐Rank for ranking heuristic ARM approaches, and based on that, ARMGA, ASC, and Kua emerge as the best approaches. We also consider ARM algorithms and their characteristics as transactions and items in a transactional database, respectively, and generate association rules that indicate research trends in this area.This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

In the recent years, the growth of online marketplace explodes and has become the new norm of shopping. Access to the internet allows consumers to visit local and global online marketplace. By using online shopping, consumers can view the latest products and comparing products' prices. Online marketplace offers flexibility and eases consumers in so many ways. On the contrary, it also has some limitations. Dynamic pricing allows sellers to enhance their marketing strategy by ensuring price competitive with other sellers. The frequent occurrence of real-time price changing limits the user to get the best deal. This paper discusses the analysis of price changes using Association Rule Mining. Price-ChARM finds a frequent pattern of price changes in corresponding to different timelines. On the online marketplace, due to multiple sellers can sell the same item with different offers such as delivery speed and dynamic pricing, the comparison can be quite tricky. We collected data from two well-known portals and we represent the dataset as set of prices for different products for the purpose of frequent itemset mining. We implement the Apriori algorithm and use a total amount of 3,960 records. We generate association rules from the frequent itemsets found in the records and visualize the confidence of 0.9 rules. The association rules represent the pattern of price changes in the portals. This study eases consumer shopping experiences by understanding the trend of price changes to provide a better decision in making a purchase. Â

One of the most important data mining problems is mining association rules. In this paper we consider discovering association rules from large transaction databases. The problem of discovering association rules can be decomposed into two sub-problems: find large itemsets and generate association rules from large itemsets. The second sub-problem is easier one and the complexity of discovering association rules is determined by complexity of discovering large itemsets. In this paper, we suggest Apriori-based algorithm for discovering large itemsets. Actually, we suggest a new procedure for large itemsets generation which is more efficient than the appropriate procedure of the original Apriori algorithm. For its implementation, we suggest a modified sort-merge-join algorithm, which is more efficient than nested-loop-join algorithm, which is suggested in the original Apriori algorithm. Besides, we propose a way in which Apriori Multiple finishes in just two iterations.

Data mining aims to develop a new generation of tools to intelligently assist humans in analyzing mountains of data. Over the past few years, great progress has been made in both research and applications of data mining. Data mining systems have helped many businesses by exposing previously unknown patterns in their databases, which were used to improve profits, enhance customer services, and ultimately achieve a competitive advantage. In this paper, we present our unique data mining system DM-II (Data Mining Integration and Interestingness). DM-II is a PC-based system working in Windows 95/98/NT environment. Apart from the normal components of a data mining system, DM-II has a number of unique and advanced sub-systems. These sub-systems have been applied in many real-life applications, including education applications, insurance application, accident application, disease application, drug screening application, image classification, etc. Here, we focus on discussing the following sub-systems: CBA: CBA originally stands for Classification-Based on Associations [lo]. It has now been extended with a number of other advanced features. In all, CBA has the following capabilities: Building classifiers using association rules: Traditionally, association rule mining and classifier building are regarded as two different data mining tasks. CBA unifies the two tasks. It is able to ,generate association rules and builds a classifier using a special subset of the association rules. What is significant is that CBA, in general, produces more accurate classifiers compared to the state-ofthe-art classification system C4.5 [ 161. It also helps to solve some outstanding problems with the existing classification systems. Pruning and summarizing the discovered associations: One major problem with association

The growing of ageing population causes many major negative impacts especially on pension plans and annuity providers. One of the impacts is on the exposure to longevity risk. There are various methods have been previously developed in order to quantify and classify longevity risk. However, there are some major drawbacks to these methods, especially in long-term mortality risk exposures studies. Therefore, this study is conducted in order to observe the potentiality of Association Rules Mining (ARM) in overcoming these drawbacks. The results show that ARM has an advantage of generating a less complex longevity profile based on the generated association rules; and can be used as an alternative method to the intricated statistical methods in profiling longevity risk exposure.

Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies. Artificial intelligence (AI) methods are used to efficiently extract meaningful patterns or insights from such huge amounts of data or big data. One such technique is Association Rule Mining (ARM) which is used to extract strategic information from the data. The crucial step in ARM is Frequent Itemsets Mining (FIM) followed by association rule generation. The FIM process starts by tuning the support threshold parameter from the user to produce the number of required frequent patterns. To perform the FIM process, the user applies hit and trial methods to rerun the aforesaid routine in order to receive the required number of patterns. The research community has shifted its focus towards the development of top-K most frequent patterns not using the support threshold parameter tuned by the user. Top-K most frequent patterns mining is considered a harder task than user-tuned support-threshold-based FIM. One of the reasons why top-K most frequent patterns mining techniques are computationally intensive is the fact that they produce a large number of candidate itemsets. These methods also do not use any explicit pruning mechanism apart from the internally auto-maintained support threshold parameter. Therefore, we propose an efficient TKIFIs Miner algorithm that uses depth-first search strategy for top-K identical frequent patterns mining. The TKIFIs Miner uses specialized one- and two-itemsets-based pruning techniques for topmost patterns mining. Comparative analysis is performed on special benchmark datasets, for example, Retail with 16,469 items, T40I10D100K and T10I4D100K with 1000 items each, etc. The evaluation results have proven that the TKIFIs Miner is at the top of the line, compared to recently available topmost patterns mining methods not using the support threshold parameter.

Being aware of the waiting time for selling residential units is one of the important issues in the housing sector for the majority of people, especially investors. There are several factors affecting the waiting time for selling residential units. Determining the influential factors on the time period of selling real estates can lead to an informed decision making by real estate consultants, sellers as well as those seeking to buy real estates. Using a real estate database in Iran, the present paper proposes a two-module procedure. The first module deals with implementation of association rule mining. Using the well-known association rule mining techniques namely FP-Growth, several association rules have been extracted which indicate the effective factors on the waiting time for selling residential units. Generated association rules have been evaluated based on metrics such as support, confidence and lift and finally the best rules are selected. The main objective of the second module is to develop a fuzzy inference system which can determine the factors influencing the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments. In conclusion section, a number of suggestions for future studies are presented. For example, machine learning algorithms such as neural networks, decision trees, etc. can also be used to predict the duration of residential units’ sale. The main objective of the second module is to develop a fuzzy inference system which can learn about the factors that influence the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it takes to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments.

The Apriori algorithm is a widely used method in association rule learning for mining frequent item sets in a dataset and generating association rules. This research focuses on the application of the Apriori algorithm to a dataset containing the presence or absence of 10 diseases in patients. The aim is to discover patterns and associations between these diseases which could provide insights into disease co-occurrence and help in the development of preventive strategies. The study demonstrates the potential of data mining techniques in the healthcare domain specifically in understanding disease correlations.

Inter-transactional association rules for multi-dimensional contexts for prediction and their application to studying meteorological data

Generalized association rule mining using an efficient data structure

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.

Incremental mining has attracted the attention of many researchers due to its usefulness in online applications. Many algorithms have thus been proposed for incrementally mining frequent itemsets. Maintaining a frequent-itemset lattice (FIL) is difficult for databases with large numbers of frequent itemsets, especially huge databases, due to the storage of links of nodes in the lattice. However, generating association rules from a FIL has been shown to be more effective than traditional methods such as directly generating rules from frequent itemsets or frequent closed itemsets. Therefore, when the number of frequent itemsets is not huge (i.e., they can be stored in the lattice without excessive memory overhead), the lattice-based approach outperforms approaches which mine association rules from frequent itemsets/frequent closed itemsets. However, incremental algorithms for building FILs have not yet been proposed. This paper proposes an effective approach for the maintenance of a FIL based on the pre-large concept in incremental mining. The building process of a FIL is first improved using two proposed theorems regarding the paternity relation between two nodes in the lattice. An effective approach for maintaining a FIL with dynamically inserted data is then proposed based on the pre-large and the diffset concepts. The experimental results show that the proposed approach outperforms the batch approach for building a FIL in terms of execution time.

In modern era, due to the broad applications of uncertain data, mining itemsets over uncertain databases has paying much more attention.Association Rule Mining (ARM) is a well known and most popular technique of Data Mining.It identifies itemsets from the dataset which appears frequently and generates association rules.This is the procedure which is followed by the traditional ARM it does not consider the utility of an itemsets.In real-world applications such as retail marketing, medical diagnosis, client segmentation etc., utility of itemsets is varied on various constraints such as based on cost, profit or revenue.Utility Mining intend to discover itemsets with their utilities by considering profit, quantity, cost or other user preferences.[22]High-utility itemset mining (HUIM) has thus emerged as an important research topic in data mining.But most HUIM algorithms only handle precise data, even though big data collected in reallife applications using experimental measurements or noisy sensors is often uncertain.High-Utility Rare Itemset (HURI) mining finds itemsets from a database which have their utility no less than a given minimum utility threshold and have their support less than a given frequency threshold.Identifying high-utility rare itemsets from a database can help in better business decision making by highlighting the rare itemsets which give high profits so that they can be marketed more to earn good profit.Koh and Rountree (2005) proposed a modified apriori inverse algorithm to generate rare itemsets of user interest.In this paper we propose an efficient algorithm named Mining High Utility Rare Itemsts over Uncertain Database (HURIU) .This novel approach uses the concept of apriori inverse over uncertain databases.This paper will also give the new version or extension of the algorithm HURI proposed by Jyothi et al.The implementation of an algorithm for the analysis is done on JDK 6.1 and referred the sample dataset presented by Lan Y.et al,2015[15] for uncertain database.

Association rule mining (ARM) identifies frequent itemsets from databases and generates association rules by considering each item in equal value. However, items are actually different in many aspects in a number of real applications, such as retail marketing, network log, etc. The difference between items makes a strong impact on the decision making in these applications. Therefore, traditional ARM cannot meet the demands arising from these applications. By considering the different values of individual items as utilities, utility mining focuses on identifying the itemsets with high utilities. As closure property doesn't apply to utility mining, the generation of candidate itemsets is the most costly in terms of time and memory space. In this paper, we present a Two-Phase algorithm to efficiently prune down the number of candidates and can precisely obtain the complete set of high utility itemsets. In the first phase, we propose a model that applies the transaction-weighted downward closure property on the search space to expedite the identification of candidates. In the second phase, one extra database scan is performed to identify the high utility itemsets. We also parallelize our algorithm on shared memory multi-process architecture using Common Count Partitioned Database (CCPD) strategy. We verify our algorithm by applying it to both synthetic and real databases. It performs very efficiently in terms of speed and memory cost, and shows good scalability on multiple processors, even on large databases that are difficult for existing algorithms to handle.