Abstract

Frequent pattern mining is one among the popular data mining techniques. Frequent pattern mining approaches extract interesting associations among the items in a given transactional database. The items of the transactional database can be organized as a concept hierarchy. Notably, frequent pattern mining does not distinguish the patterns by analyzing the categories of the items in a given concept hierarchy. In several applications, it is often useful to distinguish among the frequent patterns by analyzing how the items of the pattern are mapped to different categories of the concept hierarchy. In this paper, we propose a new interestingness measure, designated as diversity rank (drank), for capturing the diversity of a given pattern by analyzing the extent to which the items of the pattern are associated with the categories of the corresponding concept hierarchy. Given a transactional database over a set I of items and the corresponding concept hierarchy on I, we propose a methodology to compute the drank of the given pattern. Furthermore, by extending the notion of drank, we propose an approach to improve the diversity and accuracy of association rule-based recommender system. The results of our performance evaluation on the real-world MovieLens dataset demonstrate that the proposed diversity model extracts different kinds of patterns as compared to frequent patterns. Furthermore, our proposed recommender system approach improves the diver sit y performance w.r.t. the existing association rule-based recommender system without significantly compromising the accuracy. Overall, the proposed concept hierarchy-based diverse pattern model provides a scope to develop new approaches for improving the performance of frequent pattern mining-based applications.

Abstract

We propose an improved framework for document representation using word embeddings. The existing models represent the document as a position vector in the same word embedding space. As a result, they are unable to capture the multiple aspects as well as the broad context in the document. Also, due to their low representational power, existing approaches perform poorly at document classification. Furthermore, the document vectors obtained using such methods have uninterpretable features. In this paper, we propose an improved document representation framework which captures multiple aspects of the document with interpretable features. In this framework, a document is represented in a different feature space by representing each dimension with a potential feature word with relatively high discriminating power. A given document is modeled as the distances between the feature words and the …

Abstract

Periodic-frequent pattern mining is a challenging problem of great importance in many applications. Most previous works focused on finding these patterns in binary temporal databases and did not take into account the quantities of items within the data. This paper proposes a novel model of fuzzy periodic-frequent pattern (FPFP) that may exist in a quantitative temporal database (QTD). Finding FPFPs in QTD is a non-trivial and challenging task due to its huge search space. A novel pruning technique, called improved maximum scalar cardinality, has been introduced to effectively reduce the search space and the computational cost of finding the desired itemsets. This technique facilitates the mining of FPFPs in real-world very large databases practicable. An efficient algorithm has also been presented to find all FPFPs in a QTD. Experimental results demonstrate that the proposed algorithm is efficient. We also …

Abstract

Utility mining has been emerging as an important area in data mining. While existing works on utility mining for retail businesses have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly assume that each item occupies only one slot. Here, the slot size of a given item is the number of (integer) slots occupied by that item on the retail store shelves. However, in many real-world scenarios, the number of slots consumed by different items typically varies. Hence, this paper considers that a given item may physically occupy any fixed (integer) number of slots. Thus, we address the problem of efficiently determining the top-utility itemsets when a given number of slots is specified as input. The key contributions of our work are three fold. First, we present an efficient framework to determine the top-utility itemsets for different user-specified number of slots that need to be …

Abstract

Product placement in retail has a significant impact on the sales revenue of retailers. Hence, research efforts are being made to improve retailer revenue using high-utility pattern mining based product placement approaches. However, none of these existing approaches has explored generalized high-utility itemset mining for determining product placement in retail. The knowledge of generalized high-utility itemsets extracted from user purchase transactional database in conjunction with a product taxonomy can provide new insights about customer purchase behaviour. This work proposes the generalized utility itemset (GUI) index for retrieving generalized high-utility (revenue) itemsets. We also present a framework, which leverages the GUI index towards retail product placement to improve revenue. Our performance study using real datasets shows the effectiveness of our proposed scheme w.r.t. two existing …

Abstract

Partial Periodic itemsets are an important class of regularities that exist in a temporal database. A Partial Periodic itemset is something persistent and predictable that appears in the data. Past studies on Partial Periodic itemsets have been primarily focused on centralized databases and are not scalable for Big Data environments. One cannot ignore the advantage of scalability by using more resources. This is because we deal with large databases in a real-time environment and using more resources can increase the performance. To address the issue we have proposed a parallel algorithm by including the step of distributing transactional identifiers among the machines and mining the identical itemsets independently over the different machines. Experiments on Apache Spark’s distributed environment show that the proposed approach speeds up with the increase in a number of machines.

Abstract

Product placement in retail stores has a significant impact on the revenue of the retailer. Hence, research efforts are being made to propose approaches for improving item placement in retail stores based on the knowledge of utility patterns extracted from the log of customer purchase transactions. Another strategy to make any retail store interesting from a customer perspective is to cater to the varied requirements and preferences of customers. This can be achieved by placing a wider variety of items in the shelves of the retail store, thereby increasing the diversity of the items that are available for purchase. In this regard, the key contributions of our work are three-fold. First, we introduce the problem of concept hierarchy based diverse itemset placement in retail stores. Second, we present a framework and schemes for facilitating efficient retrieval of the diverse top-revenue itemsets based on a concept hierarchy. Third, we conducted a performance evaluation with a real dataset to demonstrate the overall effectiveness of our proposed schemes.

Abstract

Spatial High Utility Itemset Mining (SHUIM) aims to discover all itemsets in a spatiotemporal database that satisfy the userspecified minimum utility (minU til) and maximum distance (maxDist) constraints. The popular adoption and successful industrial application of SHUIM suffers from the following two limitations: (i) Since SHUIM determines the interestingness of an itemset without taking into account its support with in the data, it is sensitive to the random noise and length of the transactions within the data. Consequently, SHUIM facilitates sporadic itemsets with high utility to be generated as SHUIs. For instance, items in long transactions can combine with each other and be generated as SHUIs. (ii) SHUIM is a computationally expensive process because the generated itemsets do not satisfy the downward closure property. This paper introduces Spatial High Utility Frequent Itemset Mining (SHUFIM) to address these two issues. A SHUI in a spatiotemporal database is said to be a SHUFI if and only if its support is no less than the user-specified minimum support (minSup) constraint. The usage of minSup not only facilitates the proposed model to be tolerant to the random noise within the data, but also facilitate us to employ additional pruning techniques to reduce the computational cost. A single scan fast algorithm has also been proposed to discover all SHUFIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also demonstrate the usefulness of the proposed model with two real-world applications.

Abstract

Finding (partial) periodic patterns in time series data is a challenging problem of great importance in many applications. Due to computational reasons, most previous studies in this area have focused on the efficient discovery of periodic patterns in regular time series data. Unfortunately, these studies have limited applicability because real-world data naturally exists as an irregular time series. This paper proposes a more flexible model of periodic pattern that may be present in irregular time series. Two measures, period and period-support, were employed to determine the interestingness of a pattern in a series. The former measure captures the inter-arrival times of a pattern in a series, while the latter captures the number of periodic occurrences of a pattern in a series. A novel tree structure, called Periodic Pattern tree (PP-tree), has been introduced to record the irregular occurrences of items within the series. A pattern-growth algorithm has also been presented to find all periodic patterns from PP-tree. Experimental results demonstrate that the proposed model can find useful information, and the algorithm is efficient.

Abstract

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large databases. Typically, pattern mining is carried out from huge databases, which tend to get updated several times. Consequently, as a given database is updated, some of the patterns discovered may become invalid, while some new patterns may emerge. This has motivated significant research efforts in the area of Incremental Mining. The goal of incremental mining is to efficiently and incrementally mine patterns when a database is updated as opposed to mining all of the patterns from scratch from the complete database. Incidentally, research efforts are being made to develop incremental pattern mining algorithms for extracting different kinds of patterns such as frequent patterns, sequential patterns and utility patterns. However, none of the existing works addresses incremental mining in the context of coverage patterns, which has important applications in areas such as banner advertising, search engine advertising and graph mining. In this regard, the main contributions of this work are three-fold. First, we introduce the problem of incremental mining in the context of coverage patterns. Second, we propose the IncCMine algorithm for efficiently extracting the knowledge of coverage patterns when incremental database is added to the existing database. Third, we performed extensive experiments using two real-world click stream datasets and one synthetic dataset. The results of our performance evaluation demonstrate that our proposed IncCMine algorithm indeed improves the performance significantly w.r.t. the existing CMine algorithm. Index Terms—Data mining, Coverage patterns, Incremental mining, Knowledge discovery