Penerapan Sequential Pattern Mining pada Data Pemesanan untuk Strategi Penawaran dan Pemasaran Produk dengan Pendekatan Metode PrefixSpan

A business goes well depends on the sales of the business products and demanding for product called orders. The efforts to obtain orders is an essential process in the business, one of process is giving proper attention in the form of follow-up and offers to consumers. The large number of consumers sometimes will make difficult to set goals and follow-up deals. To get targeted and effective marketing deals could be by understanding the sequence patterns of customer orders. However, it is difficult to get the patterns in the usual reports or format. There is some method to get the ordered pattern called sequential pattern mining. In this research, tried to use PrefixSpan methods to process sequential pattern mining. This method is still relevant implemented on the order/sales report/data by using the order date and associated with the name of consumers who make order. Order/sales data is prepared and transformed in a format that becomes the input of PrefixSpan method called sequences and thn processed by machine of PrefixSpan. Testing result showed consumer order patterns containing connectedness among consumers to the others customer in ordering product. Patterns that obtained can be used as a reference to get more effective marketing strategy in a compan

Constraint-based Sequential Pattern Mining with Decision Diagrams

Constrained sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes. We introduce novel techniques for constraint-based sequential pattern mining that rely on a multi-valued decision diagram representation of the database. Specifically, our representation can accommodate multiple item attributes and various constraint types, including a number of non-monotone constraints. To evaluate the applicability of our approach, we develop an MDD-based prefix-projection algorithm and compare its performance against a typical generate-and-check variant, as well as a state-of-the-art constraint-based sequential pattern mining algorithm. Results show that our approach is competitive with or superior to these other methods in terms of scalability and efficiency.Comment: AAAI201

Negative sequential pattern mining

University of Technology, Sydney. Faculty of Engineering and Information Technology.Sequential pattern mining provides an important way to obtain special patterns from sequence data. It produces important insights on bioinformatics data, web-logs, customer transaction data, and so on. Different from traditional positive sequential pattern (PSP) mining, negative sequential pattern (NSP) mining takes negative itemsets into account besides positive ones. It would be more interesting in applications where non-occurring itemsets need to be considered. This thesis reports our previous and the latest research outcomes in this area. The contributions of the thesis are as following. • A comprehensive literature review of negative frequent pattern mining is described. • A general framework of the NSP mining is proposed. It can be used to describe the big picture of both PSP and NSP mining problems. • Three innovative algorithms are proposed to mine NSP efficiently. • Extensive experiments about the three algorithms on either synthetic or real-world datasets show that the proposed methods can find NSP efficiently. • A case study describes a real-life application on customer claims analysis in health insurance industry. Three algorithms of NSP mining are proposed in this thesis, listed as below: (1) The first algorithm Neg-GSP (Zheng, Zhao, Zuo & Cao 2009) is based on a PSP mining algorithm GSP (Srikant & Agrawal 1996). Neg-GSP deals with negative problem by introducing new methods of joining and generating candidates, which borrow ideas from GSP algorithm. And also, an effective pruning method to reduce the number of candidates is proposed as well. (2) The second one is a Genetic Algorithm based algorithm (Zheng, Zhao, Zuo & Cao 2010), which is called GA-NSP. It is proposed to find NSP with novel crossover and mutation operations, which are efficient at passing good genes on to next generations. An effective dynamic fitness function and a pruning method are also provided to improve performance. (3) The third algorithm e-NSP (Dong, Zheng, Cao, Zhao, Zhang, Li, Wei & Ou 2011) is based on the Set Theory. It mines NSP by only involving the identified PSP, without re-scanning the database. In this way, mining NSP does not require any additional database scans. It facilitates the existing PSP mining algorithms to mine NSP. It offers a new strategy for efficient mining of NSP. The results of extensive experiments about the three algorithms show that they can find NSP efficiently. They have good performance compared with some other existing NSP mining algorithms, such as PNSP (Hsueh, Lin & Chen 2008). If we compare the problem statements of the above three methods, Neg-GSP and GA-NSP share the same definitions, e-NSP uses stronger constraints since it requires clear boundary to follow the Set Theory. When comparing their performances, GA-NSP algorithm slightly outperforms Neg-GSP in terms of execution time, but it may miss some patterns in the complete result sets due to limitations of Genetic Algorithm. Apparently, e-NSP is the most efficient and effective one since it does not need to scan datasets to calculate the support of NSP. Although adding stronger constraints on e-NSP makes the search space much smaller than what it is under the normal definitions, it is still very practicable while being used in some real-life applications. Following that, NSP mining case studies coming from health insurance industry are introduced. Based on real-life customer claims datasets, we use the proposed NSP mining methods to find PSP and NSP on solving two business issues, one is in ancillary service over-service analysis, another is fraud claim detection. Both of the two case studies demonstrate the benefits gained from mining NSP

Using Answer Set Programming for pattern mining

Serial pattern mining consists in extracting the frequent sequential patterns from a unique sequence of itemsets. This paper explores the ability of a declarative language, such as Answer Set Programming (ASP), to solve this issue efficiently. We propose several ASP implementations of the frequent sequential pattern mining task: a non-incremental and an incremental resolution. The results show that the incremental resolution is more efficient than the non-incremental one, but both ASP programs are less efficient than dedicated algorithms. Nonetheless, this approach can be seen as a first step toward a generic framework for sequential pattern mining with constraints.Comment: Intelligence Artificielle Fondamentale (2014

Prefix-Projection Global Constraint for Sequential Pattern Mining

Sequential pattern mining under constraints is a challenging data mining task. Many efficient ad hoc methods have been developed for mining sequential patterns, but they are all suffering from a lack of genericity. Recent works have investigated Constraint Programming (CP) methods, but they are not still effective because of their encoding. In this paper, we propose a global constraint based on the projected databases principle which remedies to this drawback. Experiments show that our approach clearly outperforms CP approaches and competes well with ad hoc methods on large datasets

Negative-GSP: An efficient method for mining negative sequential patterns

Different from traditional positive sequential pattern mining, negative sequential pattern mining considers both positive and negative relationships between items. Negative sequential pattern mining doesn't necessarily follow the Apriori principle, and the searching space is much larger than positive pattern mining. Giving definitions and some constraints of negative sequential patterns, this paper proposes a new method for mining negative sequential patterns, called Negative-GSP. Negative-GSP can find negative sequential patterns effectively and efficiently by joining and pruning, and extensive experimental results show the efficiency of the method. © 2009, Australian Computer Society, Inc