Time-interval Sequential Patterns Research Articles

HUFTI-SPM: high-utility and frequent time-interval sequential pattern mining from transactional databases

HUFTI-SPM: high-utility and frequent time-interval sequential pattern mining from transactional databases

Distributed mining of high utility time interval sequential patterns using mapreduce approach

High Utility Sequential Pattern mining (HUSP) algorithms aim to find all the high utility sequences from a sequence database. Due to the large explosion of data, recently few distributed algorithms have been designed for mining HUSPs based on the MapReduce framework. However, the existing HUSP algorithms such as USpan, HUS-Span and BigHUSP are able to predict only the order of items, they do not predict the time between the items, that is, they do not include the time intervals between the successive items. But in a real-world scenario, time interval patterns provide more valuable information than conventional high utility sequential patterns. Therefore, we propose a distributed high utility time interval sequential pattern mining (DHUTISP) algorithm using the MapReduce approach that is suitable for big data. DHUTISP creates a novel time interval utility linked list data structure (TIUL) to efficiently calculate the utility of the resulting patterns. Moreover, two utility upper bounds, namely, remaining utility upper bound (RUUB) and co-occurrence utility upper bound (CUUB) are proposed to prune the unpromising candidates. We conducted various experiments to prove the efficiency of the proposed algorithm over both the distributed and non-distributed approaches. The experimental results show the efficiency of DHUTISP over state-of-the-art algorithms, namely, BigHUSP, AHUS-P, PUSOM and UTMining_A.

Mining Time-Interval Sequential Patterns with High Utility from Transaction Databases

The purpose of time-interval sequential pattern mining is to help superstore business managers promote product sales. Sequential pattern mining discovers the time interval patterns for items: for example, if most customers purchase product item <span class="bold">A</span>, and then buy items <span class="bold">B</span> and <span class="bold">C</span> after <span class="bold">r</span> to <span class="bold">s</span> and <span class="bold">t</span> to <span class="bold">u</span> days respectively, the time interval between <span class="bold">r</span> to <span class="bold">s</span> and <span class="bold">t</span> to <span class="bold">u</span> days can be provided to business managers to facilitate informed marketing decisions. We treat these time intervals as patterns to be mined, to predict the purchasing time intervals between <span class="bold">A</span> and <span class="bold">B</span>, as well as <span class="bold">B</span> and <span class="bold">C</span>. Nevertheless, little work considers the significance of product items while mining these time-interval sequential patterns. This work extends previous work and retains high-utility time interval patterns during pattern mining. This type of mining is meant to more closely reflect actual business practice. Experimental results show the differences between three mining approaches when jointly considering item utility and time intervals for purchased items. In addition to yielding more accurate patterns than the other two methods, the proposed UTMining_A method shortens execution times by delaying join processing and removing unnecessary records.

Discovering Time-Interval Sequential Patterns by a Pattern Growth Approach with Confidence Constraints

Sequential pattern mining is to discover frequent sequential patterns in a sequence database. The technique is applied to fields such as web click-stream mining, failure forecast, and traf- fic analysis. Conventional sequential pattern-mining approaches generally focus only the orders of items; however, the time interval between two consecutive events can be a valuable information when the time of the occurrence of an event is concerned. This study extends the concept of the well-known pattern growth approach, PrefixSpan algorithm, to propose a novel sequential pattern mining approach for sequential patterns with time intervals. Unlike the other time-interval sequential pattern-mining algorithms, the approach concerns the time for the next event to occur more than the timing information with its precedent events. To obtain a more reliable sequential pattern, a new measure of the confidence of a sequential pattern is defined. Experiments are conducted to evaluate the performance of the proposed approach.

Mining time-interval univariate uncertain sequential patterns

In this study, we propose two algorithms to discover time-interval univariate uncertain (U2) -sequential patterns from a set of univariate uncertain (U2)-sequences. A U2-sequence is a sequence that contains transactions of univariate uncertain data, where each attribute in a transaction is associated with a quantitative interval and a probability density function indicating the possibility that each value exists in the interval. Many sources record U2-sequences, such as atmospheric pollution sensors and network monitoring systems. Mining sequential patterns from these U2-sequences is important for understanding the intrinsic characteristics of the U2-sequences. The proposed two algorithms are based on the candidate generate-and-test methodology and pattern growth methodology, respectively. We performed a series of experiments to evaluate them in terms of runtime and memory consumption. The experimental results show that different algorithms excel when applied to different conditions. In general, the algorithm based on the pattern growth methodology is the better choice.

Mining the Change of Customer Behavior in Fuzzy Time-Interval Sequential Patterns with Aid of Similarity Computation Index (SCI) and Genetic Algorithm (GA)

Sequential Pattern Mining is closely related to the concept of Data Mining. It is the process of discovering the frequent sequential patterns in a given database. Even though, various algorithms and techniques are used in the process of sequential pattern mining. One of the Mine Fuzz Change model was perform the Sequential Pattern Mining process by using SCI (Similarity Computation Index) and successfully perform the pattern classification process. But this model has the drawback in the SCI computation. Because, the SCI value is computed by using the raw data which is collected from different time interval and it creates the time complexity in the pattern mining process. To solve this drawback, in this paper, an optimized fuzzy time interval sequential pattern mining algorithm is proposed. The proposed method finds the customer behavior changes in the fuzzy time interval sequential patterns by exploiting the optimized FTI algorithm and the patterns are classified based on their support and SCI (Similarity Computation Index) value. Our proposed method performance is evaluated by conducting different experiments on the synthetic dataset. Moreover, our proposed method performance is compared with the existing Mine Fuzz Change model. The performance results shows that our proposed method reduces the time complexity and it will helps managers to understand the changing behaviors of their customers

A time-interval sequence classification method

Classification is one of the most popular behavior prediction tools in behavior informatics (behavior computing) to predict group membership for data instances. It has been greatly used to support customer relationship management (CRM) such as customer identification, one-to-one marketing, fraud detection, and lifetime value analysis. Although previous studies showed themselves efficient and accurate in certain CRM classification applications, most of them took demographic, RFM-type, or activity attributes as classification criteria and seldom took temporal relationship among these attributes into account. To bridge this gap, this study takes customer temporal behavior data, called time-interval sequences, as classification criteria and develops a two-stage classification framework. In the first stage, time-interval sequential patterns are discovered from customer temporal databases. Then, a time-interval sequence classifier optimized by the particle swam optimization (PSO) algorithm is developed to achieve high classification accuracy in the second stage. The experiment results indicate the proposed time-interval sequence classification framework is efficient and accurate to predict the class label of new customer temporal data.

Mining approximate Time-interval sequential pattern in data stream

Mining approximate Time-interval sequential pattern in data stream

Mining the change of customer behavior in fuzzy time-interval sequential patterns

Comprehending changes of customer behavior is an essential problem that must be faced for survival in a fast-changing business environment. Particularly in the management of electronic commerce (EC), many companies have developed on-line shopping stores to serve customers and immediately collect buying logs in databases. This trend has led to the development of data-mining applications. Fuzzy time-interval sequential pattern mining is one type of serviceable data-mining technique that discovers customer behavioral patterns over time. To take a shopping example, (Bread, Short, Milk, Long, Jam), means that Bread is bought before Milk in a Short period, and Jam is bought after Milk in a Long period, where Short and Long are predetermined linguistic terms given by managers. This information shown in this example reveals more general and concise knowledge for managers, allowing them to make quick-response decisions, especially in business. However, no studies, to our knowledge, have yet to address the issue of changes in fuzzy time-interval sequential patterns. The fuzzy time-interval sequential pattern, (Bread, Short, Milk, Long, Jam), became available in last year; however, is not a trend this year, and has been substituted by (Bread, Short, Yogurt, Short, Jam). Without updating this knowledge, managers might map out inappropriate marketing plans for products or services and dated inventory strategies with respect to time-intervals. To deal with this problem, we propose a novel change mining model, MineFuzzChange, to detect the change in fuzzy time-interval sequential patterns. Using a brick-and-mortar transactional dataset collected from a retail chain in Taiwan and a B2C EC dataset, experiments are carried out to evaluate the proposed model. We empirically demonstrate how the model helps managers to understand the changing behaviors of their customers and to formulate timely marketing and inventory strategies.

Generating touring path suggestions using time-interval sequential pattern mining

Guiding service plays an important role for visitors to comprehend exhibits in exhibitions or museums. Without appropriate guiding service, visitors might miss some popular exhibits or cannot complete the visiting trip in time. Therefore, how to provide visitors customized touring service becomes an important task for exhibitions. To bridge the gap, this research takes previous popular visiting behaviors as the suggestion foundation and develops a sequential pattern mining based touring path suggestion system to generate personalized tours. In the exhibition, visiting sequences are persistently collected through a Radio-Frequency Identification (RFID) system. Next, the time-interval sequential pattern mining (I-PrefixSpan) algorithm is applied to obtain popular touring paths containing not only the visiting sequences but also the visiting time between exhibits. Based on the visitor’s personal profile, the system retrieves a set of candidate touring paths. Then, the suggestion priority of each candidate touring path is evaluated based on the number of sections the path passed through, the number of exhibits the path passed through, and time closeness between the path spend and the visitor’s intended visiting time. Finally, the Nation Museum of History in Taiwan is used as an example to show the feasibility of the proposed method.

A TIME-INTERVAL SEQUENTIAL PATTERN CHANGE DETECTION METHOD

Several studies have focused on mining changes in different time-period databases. Analyzing these change behaviors provides useful information for managers to develop better marketing strategies and decision making. Although some researchers have developed efficient methods for association rule change detection, no attempt has been made to analyze time-interval sequential pattern changes in databases collected over time. Therefore, this research proposes a time-interval sequential pattern change detection framework to derive the change trends in customer behaviors in two periods. First, two time-interval sequential pattern sets are generated from two time-period databases respectively using the proposed DTI-Apriori algorithm. Different from previous mining methods that require users to manually define a set of time-interval ranges in advance, the DTI-Apriori algorithm automatically arranges the time-interval range and then generates time-interval sequential patterns. The degree of change for each pair of time-interval sequential patterns from different time periods is evaluated next. Based on the degree of change, a time-interval sequential pattern is clarified as one of the following three change types: an emerging time-interval sequential pattern, an unexpected time-interval sequential pattern, or an added/perished time-interval sequential pattern. Significant change patterns are returned to users for further analysis if the degree of change is large enough.

Mining Target-Oriented Sequential Patterns With Time-Intervals

A target-oriented sequential pattern is a sequential pattern with a concerned itemset in the end of pattern. A time-interval sequential pattern is a sequential pattern with time-intervals between every pair of successive itemsets. In this paper we present an algorithm to discover target-oriented sequential pattern with time-intervals. To this end, the original sequences are reversed so that the last itemsets can be arranged in front of the sequences. The contrasts between reversed sequences and the concerned itemset are then used to exclude the irrelevant sequences. Clustering analysis is used with typical sequential pattern mining algorithm to extract the sequential patterns with time-intervals between successive itemsets. Finally, the discovered time-interval sequential patterns are reversed again to the original order for searching the target patterns.

On mining multi-time-interval sequential patterns

Sequential pattern mining is essential in many applications, including computational biology, consumer behavior analysis, web log analysis, etc. Although sequential patterns can tell us what items are frequently to be purchased together and in what order, they cannot provide information about the time span between items for decision support. Previous studies dealing with this problem either set time constraints to restrict the patterns discovered or define time-intervals between two successive items to provide time information. Accordingly, the first approach falls short in providing clear time-interval information while the second cannot discover time-interval information between two non-successive items in a sequential pattern. To provide more time-related knowledge, we define a new variant of time-interval sequential patterns, called multi-time-interval sequential patterns, which can reveal the time-intervals between all pairs of items in a pattern. Accordingly, we develop two efficient algorithms, called the MI-Apriori and MI-PrefixSpan algorithms, to solve this problem. The experimental results show that the MI-PrefixSpan algorithm is faster than the MI-Apriori algorithm, but the MI-Apriori algorithm has better scalability in long sequence data.

Discovering Fuzzy Time-Interval Sequential Patterns in Sequence Databases

Given a sequence database and minimum support threshold, the task of sequential pattern mining is to discover the complete set of sequential patterns in databases. From the discovered sequential patterns, we can know what items are frequently brought together and in what order they appear. However, they cannot tell us the time gaps between successive items in patterns. Accordingly, Chen et al. have proposed a generalization of sequential patterns, called time-interval sequential patterns, which reveals not only the order of items, but also the time intervals between successive items. An example of time-interval sequential pattern has a form like (A, I2, B, I1, C), meaning that we buy A first, then after an interval of I2 we buy B, and finally after an interval of I1 we buy C, where I2 and I1 are predetermined time ranges. Although this new type of pattern can alleviate the above concern, it causes the sharp boundary problem. That is, when a time interval is near the boundary of two predetermined time ranges, we either ignore or overemphasize it. Therefore, this paper uses the concept of fuzzy sets to extend the original research so that fuzzy time-interval sequential patterns are discovered from databases. Two efficient algorithms, the fuzzy time interval (FTI)-Apriori algorithm and the FTI-PrefixSpan algorithm, are developed for mining fuzzy time-interval sequential patterns. In our simulation results, we find that the second algorithm outperforms the first one, not only in computing time but also in scalability with respect to various parameters.

Discovering time-interval sequential patterns in sequence databases

Abstract Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, in an important data-mining problem with broad applications. Although conventional sequential patterns can reveal the order of items, the time between items is not determined; that is, a sequential pattern does not include time intervals between successive items. Accordingly, this work addresses sequential patterns that include time intervals, called time-interval sequential patterns. This work develops two efficient algorithms for mining time-interval sequential patterns. The first algorithm is based on the conventional Apriori algorithm, while the second one is based on the PrefixSpan algorithm. The latter algorithm outperforms the former, not only in computing time but also in scalability with respect to various parameters.