data mining techniques have been used widely in the area of customer relationship management (crm). in this study, we have applied data mining techniques to address a problem in business-to-business (b2b) setting. in a manufacturer-retailer-consumer chain, a manufacturer should improve its relationship with retailers to continue its business. segmentation is a useful tool for identifying groups...

A new framework for analyzing time series data called Time Series Data Mining (TSDM) is introduced. This framework adapts and innovates data mining concepts to analyzing time series data. In particular, it creates methods that reveal hidden temporal patterns that are characteristic and predictive of time series events. The TSDM framework, concepts, and methods, which use a genetic algorithm to ...

In real world, the huge amount of temporal data is to be processed in many application areas such as scientific, financial, network monitoring, sensor data analysis. Data mining techniques are primarily oriented to handle discrete features. In the case of temporal data the time plays an important role on the characteristics of data. To consider this effect, the data discretization techniques ha...

An important usage of time sequences is for discovering temporal patterns of events (a special type of data mining). This process usually starts with the speciication by the user of an event structure which consists of a number of variables representing events and temporal constraints among these variables. The goal of the data mining is to nd temporal patterns, i.e., instantiations of the vari...

A spatial co-orientation pattern refers to objects that frequently occur with the same spatial orientation, e.g. left, right, below, etc., among images. In this paper, we introduce temporal co-orientation pattern mining which is the problem of temporal aspects of spatial co-orientation patterns. A temporal coorientation pattern represents how spatial co-orientation patterns change over time. Te...

Data mining can be used to extensively automate the data analysis process. Techniques for mining interval time series, however, have not been considered. Such time series are common in many applications. In this paper, we investigate mining techniques for such time series. Specifically, we propose a technique to discover temporal containment relationships. An item A is said to contain an item B...

Temporal networks are ubiquitous and evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Although many relational datasets contain temporal information, the majority of existing techniques in relational learning focus on static snapshots and ignore the temporal dynamics. We propose a framework for discovering temporal representations of relational data to i...

In many temporally oriented applications, it is known that events have occurred but the exact time when an event has occurred is not known. For example, a blood test of a diabetic patient may yield information that the patient's blood glucose level is above the safe threshold but may not exactly tell when that has happened. Such temporal events are said to have valid-time indeterminacy, where t...

Spatio-temporal data usually records the states over time of an object, an event or a position in space. Spatio-temporal data can be found in several application fields, such as traffic management, environment monitoring, weather forecast, etc. In the past, huge effort was devoted to spatial data representation and manipulation with particular focus on its visualisation. More recently, the inte...