Mining Changes for Real-Life Applications

Liu, Bing; Hsu, Wynne; Han, H.-S.; Xia, Yiyuan

doi:10.1007/3-540-44466-1_34

Cited by 60 publications

(38 citation statements)

References 4 publications

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“…In Table 2, in the first and second cases (minsim = 0.6, minentro = 6 and minsim = 0.75, minentro = 6), all the bridging rules belong to the clusters mammal and Table 2 The results generated by the joint-entropy-based strategy (25,82), (3,77), (3,20), (106, 125), (61, 67), (24,125), (115, 125), (72, 125), mininfo = 1.5, p = 1 (122, 256), (19,33), (19,67), (25,100), (25,73) (19,32), (19,67), (19,33), (3,123), (27,78) Table 4 The results generated by the joint-entropy-based strategy Thresholds Bridging rules minsim = 0.6, minentro = 6.2 (18,47), (6,44), (26,44), (18,44), (30,44), (18,42), (22,42), (17,44), (2,41), (28,38), (29,42), (1...…”

Section: Zoo Databasementioning

confidence: 98%

“…The bridging rules are different from them, because they are based on conceptual clusters and represent the interactions between different clusters. At the same time, although bridging rules employ the same form of association rules as other patterns, including unexpected patterns [7], exceptional patterns [6], negative association rules [10], change mining [17] and finding interesting patterns using user expectations [18], there are significant differences between them. Other related research efforts include [33][34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

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Mining bridging rules between conceptual clusters

Zhang

Chen

et al. 2010

Appl Intell

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Bridging rules take the antecedent and action from different conceptual clusters. They are distinguished from association rules (frequent itemsets) because (1) they can be generated by the infrequent itemsets that are pruned in association rule mining, and (2) they are measured by their importance including the distance between two conceptual clusters, whereas frequent itemsets are measured only by their support. In this paper, we first design two algorithms for mining bridging rules between clusters, and then propose two non-linear metrics to measure their interestingness. We evaluate these algorithms experimentally and demonstrate that our approach is promising.

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Section: Zoo Databasementioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Mining bridging rules between conceptual clusters

Zhang

Chen

et al. 2010

Appl Intell

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“…Others have compared decision trees learned with two related data sets. Several C4.5-based variants of this technique have been introduced [6].…”

Section: Change Detectionmentioning

confidence: 99%

Visualization of Cluster Changes by Comparing Self-organizing Maps

Denny

Squire

2005

Advances in Knowledge Discovery and Data Mining

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Abstract. In this paper we introduce Self-Organizing Map-based techniques that can reveal structural cluster changes in two related data sets from different time periods in a way that can explain the new result in relation to the previous one. These techniques are demonstrated using a real-world data set from the World Development Indicators database maintained by the World Bank. The results verify that the methods are capable of revealing changes in cluster strucure and membership, corresponding to known changes in economic fortunes of countries.

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“…Understanding and adapting to changes of customer behaviors is an important aspect of surviving in a continuously changing environment. For a modern company, knowing what is changing and how it has been changed is of crucial importance because it allows businesses to provide the right products and services to suit the changing market needs [1] . For examples, most decision makers in many companies need to know the answers to following questions: Which customer group's sales are gradually increasing?…”

Section: Introductionmentioning

confidence: 99%

Mining Customer Change Model Based on Swarm Intelligence

Jin

Zhu

2007

Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence

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Abstract. Understanding and adapting to changes of customer behavior is an important aspect of surviving in a continuously changing market environment for a modern company. The concept of customer change model mining is introduced and its process is analyzed in this paper. A customer change model mining method based on swarm intelligence is presented, and the strategies of pheromone updating and items searching are given. Finally, an examination on two customer datasets of a telecom company illuminates that this method can achieve customer change model efficiently.

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Mining Changes for Real-Life Applications

Cited by 60 publications

References 4 publications

Mining bridging rules between conceptual clusters

Mining bridging rules between conceptual clusters

Visualization of Cluster Changes by Comparing Self-organizing Maps

Mining Customer Change Model Based on Swarm Intelligence

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