Mining Negative Sequential Patterns in Transaction Databases

In data mining field, sequential pattern mining can be applied in divers applications such as basket analysis, web access patterns analysis, and quality control in manufactory engineering, etc. Many methods have been proposed for mining sequential patterns. However, conventional methods only consider the occurrences of itemsets in customer sequences. The sequential patterns discovered by these methods are called as positive sequential patterns, i.e., such sequential patterns only represent the occurrences of itemsets. In practice, the absence of a frequent itemset in a sequence may imply significant information. We call a sequential pattern as negative sequential pattern, which also represents the absence of itemsets in a sequence. The two major difficulties in mining sequential patterns, especially negative ones, are that there may be huge number of candidates generated, and most of them are meaningless. In this paper, we proposed a method for mining strong positive and negative sequential patterns, called PNSPM. In our method, the absences of itemsets are also considered. Besides, only sequences with high degree of interestingness will be selected as strong sequential patterns. An example was taken to illustrate the process of PNSPM. The result showed that PNSPM could prune a lot of redundant candidates, and could extract meaningful sequential patterns from a large number of frequent sequences.

Mining actionable repetitive positive and negative sequential patterns

Positive and negative sequential patterns (PNSP) play an informative role in various applications. In this paper, a new method is proposed to effectively select the actionable sequential patterns (ASP) from the PNSPs by segmenting and discriminating elements with sequence. First, it is to locally discriminate adjacent elements and incremental elements in the PNSPs. Second, globally segment and discriminate all the elements with sequences. Third, Markov process is further applied to select the ASP by measuring the interestingness of a sequence. The experimental comparisons on synthetic and real-world databases show that the proposed method is very effective to select ASPs.

Campus data analysis is becoming increasingly important in mining students’ behavior. The consumption data of college students is an important part of the campus data, which can reflect the students’ behavior to a great degree. A few methods have been used to analyze students’ consumption data, such as classification, association rules, clustering, decision trees, time series, etc. However, they do not use the method of sequential patterns mining, which results in some important information missing. Moreover, they only consider the occurring (positive) events but do not consider the nonoccurring (negative) events, which may lead to some important information missing. So this paper uses a positive and negative sequential patterns mining algorithm, called NegI-NSP, to analyze the consumption data of students. Moreover, we associate students’ consumption data with their academic grades by adding the students’ academic grades into sequences to analyze the relationship between the students’ academic grades and their consumptions. The experimental results show that the students’ academic performance has significant correlation with the habits of having breakfast regularly.

Sequential pattern is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining sequential patterns are built on the binary attributes databases, which has two limitations. First, it can not concern quantitative attributes; second, only direct sequential patterns are discovered. Mining fuzzy sequential patterns has been proposed to address the first limitation. In this paper, we put forward a discovery algorithm for mining indirect sequential patterns to resolve the second limitation, and a discovery algorithm for mining both direct and indirect fuzzy sequential patterns by combining these two approaches.

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.

One of the critical abilities of intelligent agents is making quick, accurate and wise decision within dynamic environments in a reasonable period of time. This paper introduces a new sequence classification method based on positive and negative sequential patterns. The historical log data from previous performance “TAC/Ad auction” has been used for the classification. We have applied Nconf: an “interestingness measure” to prune meaningless negative and positive candidates. Our experiments showed that agents equipped with this classifier can earn higher profit compared to non equipped ones. Our findings lead us to believe that with some additional tune during the run we may achieve more qualified agents.

Mining frequent sequential patterns from sequence databases has been a central research topic in data mining and various efficient mining sequential patterns algorithms have been proposed and studied. Recently, a novel sequential pattern mining research, called mining repetitive gapped subsequences, has attracted the attention of many researchers. However, the number of repetitive gapped subsequences generated by even these closed mining algorithms may be too large to understand for users, especially when support threshold is low. In this paper, we propose the problem of how to compress repetitive gapped sequential patterns. A novel distance measure of repetitive gapped sequential patterns and an efficient representative pattern checking scheme, δ-dominate sequential pattern checking are proposed. We also develop an efficient algorithm, CRGSgrow ( Compressing Repetitive Gapped Sequential pattern grow), including an efficient pruning strategy, SyncScan. An empirical study with both real and synthetic data sets clearly shows that the CRGSgrow has good performance.Keywordsrepetitive gapped sequential patterncompressing frequent patterns

NSPM is significant, and at certain times, it is much more informative when compared to PSP, and it could be exploited in numerous intelligent systems. Accordingly, only certain researches on NSP mining methodologies are obtainable, and the majority is very incompetent as they attain the support of NSC by scrutinizing the database frequently. Anyhow, when datasets turn out to be dense, the key procedure to attain the NSC support in NSP will consume more time, and it requires to be enhanced. Accordingly, this survey intends to review various topics to solve NSPM issues. Accordingly, the performance measures and the maximum performance achievements are also analyzed and demonstrated in this survey. In addition, the algorithmic classification for the surveyed papers is analyzed and described. Finally, the research gaps and issues of the NSPM issues are also discussed briefly.

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e‐learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up‐to‐date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high‐utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open‐source libraries of itemset mining implementations are also briefly presented.WIREs Data Mining Knowl Discov2017, 7:e1207. doi: 10.1002/widm.1207This article is categorized under:Algorithmic Development > Association RulesTechnologies > Association Rules

Data mining has been defined as the non- trivial extraction of implicit, previously unknown and potentially useful information from data. Association mining and sequential mining analysis are considered as crucial components of strategic control over a broad variety of disciplines in business, science and engineering. Association mining is one of the important sub-fields in data mining, where rules that imply certain association relationships among a set of items in a transaction database are discovered. In Sequence mining, data are represented as sequences of events, where order of those events is important. Finding patterns in sequences is valuable for predicting future events. In many applications such as the WEB applications, stock market, and genetic analysis, finding patterns in a sequence of elements or events, helps in predicting what could be the next event or element. At the conceptual level, association mining and sequence mining are two similar processes but using different representations of data. In association mining, items are distinct and the order of items in a transaction is not important. While in sequential pattern mining, the order of elements (events) in transactions (sequences) is important, and the same event may occur more than once. In this paper, we propose a new mapping function that maps event sequences into itemsets. Based on the unified representation of the association mining and the sequential pattern, a new approach that uses the Boolean representation of input database D to build a Boolean matrix M. Boolean algebra operations are applied on M to generate all frequent itemsets. Finally, frequent items or frequent sequential patterns are represented by logical expressions that could be minimized by using a suitable logical function minimization technique.

Comparative analysis of sequence weighting approaches for mining time-interval weighted sequential patterns

Recently, HIC has formulated its strategic direction for the 21st century. In the new direction, HIC will not only serve as a payment agency but also become a health information provider to the public. Following the new strategic direction, HIC has set up a new Information Management Division to fulfil its goals. The research section within the new division has entered a new research area for data mining applications to the management of diseases. This is an attempt to apply data mining techniques to analyze the medical service transaction data for disease management rather than the data used in the well-studied applications to medical diagnosis. The disease management project undergoing at The Health Insurance Commission of Australia is aimed at identifying good or not so good medical service practice from a large transaction database. In this paper, we present a case study on our application to diabetes management. In this case study, we selected 7443 diabetes patients living in Fairfeld and extracted their Medicare service data in 1998 from the Medicare transactional database item sequences. After selecting the 26 most frequently used items, we further transferred the patients’ item sequences into a set of vectors, each components of which represent a group of services. The choose four service groups which are identified by the Clinic Advisory Group (CAG) of Diabetes to be the most essential tests for diabetic patients.

In this chapter, we present granule based inter association mining to reduce the complexity of intertransaction association rule mining. To compare with other methods, our method can reduce the width of sliding windows. It uses granules to replace extended item sets. Thus, we do not need to consider too many combinations of extended items. We also propose the concept of precision in order to evaluate the effectiveness of intertransaction association rule mining. The experiments show that the proposed method is promising.

Mining sequential patterns is to discover sequential purchasing behaviours for most of the customers from a large number of customer transactions. The strategy of mining sequential patterns focuses on discovering frequent sequences. A frequent sequence is an ordered list of the itemsets purchased by a sufficient number of customers. The previous approaches for mining sequential patterns need to repeatedly scan the database so that they take a large amount of computation time to find frequent sequences. The customer transactions will grow rapidly in a short time, and some of the customer transactions may be antiquated. Consequently, the frequent sequences may be changed due to the insertion of new customer transactions or the deletion of old customer transactions from the database. It may require rediscovering all the patterns by scanning the entire updated customer transaction database. In this paper, we propose an incremental updating technique to maintain the discovered sequential patterns when transactions are inserted into or deleted from the database. Our approach partitions the database into some segments and scans the database segment by segment. For each segment scan, our approach prunes those sequences that cannot be frequent sequences any more to accelerate the finding process of the frequent sequences. Therefore, the number of database scans can be significantly reduced by our approach. The experimental results show that our algorithms are more efficient than other algorithms for the maintenance of mining sequential patterns.