Constraining the Search Space in Temporal Pattern Mining

Agents in dynamic environments have to deal with complex situations including various temporal interrelations of actions and events. Discovering frequent patterns in such scenes can be useful in order to create prediction rules which can be used to predict future activities or situations. We present the algorithm MiTemP which learns frequent patterns based on a time intervalbased relational representation. Additionally the problem has also been transfered to a pure relational association rule mining task which can be handled by WARMR. The two approaches are compared in a number of experiments. The experiments show the advantage of avoiding the creation of impossible or redundant patterns with MiTemP. While less patterns have to be explored on average with MiTemP more frequent patterns are found at an earlier refinement level

On mining complex sequential data by means of FCA and pattern structures

Nowadays data sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of "complex" sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of Formal Concept Analysis (FCA) and its extension based on "pattern structures". Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e., a data reduction of sequential structures), are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analyzing interesting patient patterns from a French healthcare data set on cancer. The quantitative and qualitative results (with annotations and analysis from a physician) are reported in this use case which is the main motivation for this work. Keywords: data mining; formal concept analysis; pattern structures; projections; sequences; sequential data.Comment: An accepted publication in International Journal of General Systems. The paper is created in the wake of the conference on Concept Lattice and their Applications (CLA'2013). 27 pages, 9 figures, 3 table

Association Rule Algorithm Sequential Pattern Discovery using Equivalent Classes (SPADE) to Analyze the Genesis Pattern of Landslides in Indonesia

Landslide is one of movement of soil, rock, soil creep, and rock debris that occurred the move of the slopes. It is caused by steep slopes, high rainfall, deforestation, mining activities, and erosion. The impacts of the landslide are loss of property, damage to facilities such as homes and buildings, casualties, psychological trauma, disrupted economic and environmental damage. Based on the impacts of landslide, mitigation required to take early precautions are to know how the pattern of association between the sequence of events landslides and to know how the associative relationship pattern of earthquakes. Based on the impacts, the results of this research is associative relationship pattern is obtained from data flood that occurs in Indonesia, namely in case of heavy rain will occur labile soil structure to support the value of 0.37, confidence level of 41% and the power of formed ruled is 1.02