Re-mining item associations: methodology and a case study in apparel retailing

Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analysis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analysis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques

Fast and Accurate Mining of Correlated Heavy Hitters

The problem of mining Correlated Heavy Hitters (CHH) from a two-dimensional data stream has been introduced recently, and a deterministic algorithm based on the use of the Misra--Gries algorithm has been proposed by Lahiri et al. to solve it. In this paper we present a new counter-based algorithm for tracking CHHs, formally prove its error bounds and correctness and show, through extensive experimental results, that our algorithm outperforms the Misra--Gries based algorithm with regard to accuracy and speed whilst requiring asymptotically much less space

Grafting for Combinatorial Boolean Model using Frequent Itemset Mining

This paper introduces the combinatorial Boolean model (CBM), which is defined as the class of linear combinations of conjunctions of Boolean attributes. This paper addresses the issue of learning CBM from labeled data. CBM is of high knowledge interpretability but na\"{i}ve learning of it requires exponentially large computation time with respect to data dimension and sample size. To overcome this computational difficulty, we propose an algorithm GRAB (GRAfting for Boolean datasets), which efficiently learns CBM within the $L_1$-regularized loss minimization framework. The key idea of GRAB is to reduce the loss minimization problem to the weighted frequent itemset mining, in which frequent patterns are efficiently computable. We employ benchmark datasets to empirically demonstrate that GRAB is effective in terms of computational efficiency, prediction accuracy and knowledge discovery

A Survey on Index Support for Item Set Mining

It is very difficult to handle the huge amount of information stored in modern databases. To manage with these databases association rule mining is currently used, which is a costly process that involves a significant amount of time and memory. Therefore, it is necessary to develop an approach to overcome these difficulties. A suitable data structures and algorithms must be developed to effectively perform the item set mining. An index includes all necessary characteristics potentially needed during the mining task; the extraction can be executed with the help of the index, without accessing the database. A database index is a data structure that enhances the speed of information retrieval operations on a database table at very low cost and increased storage space. The use index permits user interaction, in which the user can specify different attributes for item set extraction. Therefore, the extraction can be completed with the use index and without accessing the original database. Index also supports for reusing concept to mine item sets with the use of any support threshold. This paper also focuses on the survey of index support for item set mining which are proposed by various authors

Mining Event Logs to Support Workflow Resource Allocation

Workflow technology is widely used to facilitate the business process in enterprise information systems (EIS), and it has the potential to reduce design time, enhance product quality and decrease product cost. However, significant limitations still exist: as an important task in the context of workflow, many present resource allocation operations are still performed manually, which are time-consuming. This paper presents a data mining approach to address the resource allocation problem (RAP) and improve the productivity of workflow resource management. Specifically, an Apriori-like algorithm is used to find the frequent patterns from the event log, and association rules are generated according to predefined resource allocation constraints. Subsequently, a correlation measure named lift is utilized to annotate the negatively correlated resource allocation rules for resource reservation. Finally, the rules are ranked using the confidence measures as resource allocation rules. Comparative experiments are performed using C4.5, SVM, ID3, Na\"ive Bayes and the presented approach, and the results show that the presented approach is effective in both accuracy and candidate resource recommendations.Comment: T. Liu et al., Mining event logs to support workflow resource allocation, Knowl. Based Syst. (2012), http://dx.doi.org/ 10.1016/j.knosys.2012.05.01

Mining frequent intemsets in memory-resident databases

Due to the present-day memory sizes, a memory-resident database has become a practical option. Consequently, new methods designed to mining in such databases are desirable. In the case of disk-resident databases, breadth-first search methods are commonly used. We propose a new algorithm, based upon depth-first search in a set-enumeration tree. For memory-resident databases, this method turns out to be superior to breadth-first search

An Enhanced Approach for Compress Transaction Databases

Associative rule mining is defined as the task that deals with the extraction of hidden knowledge and frequent patterns from very large databases. Traditional associative mining processes are iterative, time consuming and storage expensive. To solve these processes, a way of representation that reduces this size and at the same time maintains all the important and relevant data needed to extract the desired knowledge from transaction databases is needed. This paper proposes a method that merges the transactions in the transaction database and uses FP-Growth algorithm for mining associative knowledge is presented. The experimental results in terms of compression ratio, both in terms of storage required and number of transactions, prove that the proposed algorithm is an improved version to the existing systems

Structural advances for pattern discovery in multi-relational databases

With ever-growing storage needs and drift towards very large relational storage settings, multi-relational data mining has become a prominent and pertinent field for discovering unique and interesting relational patterns. As a consequence, a whole suite of multi-relational data mining techniques is being developed. These techniques may either be extensions to the already existing single-table mining techniques or may be developed from scratch. For the traditionalists, single-table mining algorithms can be used to work on multi-relational settings by making inelegant and time consuming joins of all target relations. However, complex relational patterns cannot be expressed in a single-table format and thus, cannot be discovered. This work presents a new multi-relational frequent pattern mining algorithm termed Multi-Relational Frequent Pattern Growth (MRFP Growth). MRFP Growth is capable of mining multiple relations, linked with referential integrity, for frequent patterns that satisfy a user specified support threshold. Empirical results on MRFP Growth performance and its comparison with the state-of-the-art multirelational data mining algorithms like WARMR and Decentralized Apriori are discussed at length. MRFP Growth scores over the latter two techniques in number of patterns generated and speed. The realm of multi-relational clustering is also explored in this thesis. A multi-Relational Item Clustering approach based on Hypergraphs (RICH) is proposed. Experimentally RICH combined with MRFP Growth proves to be a competitive approach for clustering multi-relational data. The performance and iii quality of clusters generated by RICH are compared with other clustering algorithms. Finally, the thesis demonstrates the applied utility of the theoretical implications of the above mentioned algorithms in an application framework for auto-annotation of images in an image database. The system is called CoMMA which stands for Combining Multi-relational Multimedia for Associations