A competitive “wavelet layer” is proposed for pattern clustering. It exploits the representation capabilities of adaptive wavelets to generate template approximations for each cluster of data. A brief review of adaptive wavelet representations, as well as some insight into local minima problems, is provided. The method is illustrated by a simple clustering problem, in which step responses of dy...

Analysing and mining image data to derive potentially useful information is a very challenging task. Image mining concerns the extraction of implicit knowledge, image data relationships, associations between image data and other data or patterns not explicitly stored in the images. Another crucial task is to organise the large image volumes to extract relevant information. In fact, decision sup...

In this paper we consider the problem of finding subconcepts of a known concept (reference concept) in a given ontology in the light of new knowledge coming from a data source. These subconcepts are discovered by looking for frequent association patterns between the reference concept and other concepts also occurring in the existing ontology. As an illustration, we report preliminary results ob...

For the past decade, the need of multimedia mining has increased tremendously, especially in image data due to inexpensive digital technologies and fast mounting of image data. In this paper, we, first, show an algorithm, SpIBag (Spatial Item Bag Mining), which discovers frequent spatial patterns in images. Due to the properties of image data, SpIBag considers a bag of items together with a spa...

We present a method for automatically clustering similar attribute values in a database system spanning mulitple domains. The method constructs an aftribute abstraction hierarchy for each attribute using rules that are derived from the database instance. The rules have a confidence and popularity that combine to express the "usefullness" of the rule. Attribute values are clustered if they are u...

We propose a method for clustering web transaction data based on the idea that patterns generated within a cluster are similar to each other and different from patterns generated from other clusters. To do this, we define the difference between clusters and the similarity of transactions within a cluster using the notion of itemsets. A preliminary experiment on user-centric web browsing data de...

Detecting crime from data analysis can be difficult because daily activities of criminal generate large amounts of data. The police records exist in various formats and the quality of analysis greatly depends on the background knowledge of the analyst. This paper proposes a simple correlation clustering algorithm which aims at finding illegal activities of professional identity fraudsters based...

Clustering is a typical method of grouping data points in an unsupervised learning environment. The performance of most clustering algorithms is dependent on the accurate estimate of the cluster number, which is always unknown in the real applications. In this paper, we propose a new parametric approach, which starts with an estimate of the local distribution and efficiently avoids pre-assuming...

For the past decade, the need of multimedia mining has increased tremendously, especially in image data due to inexpensive digital technologies and fast mounting of image data. In this paper, we, first, show an algorithm, SpIBag (Spatial Item Bag Mining), which discovers frequent spatial patterns in images. Due to the properties of image data, SpIBag considers a bag of items together with a spa...

In this paper, we propose CPHC, a semi-supervised classification algorithm that uses a pattern-based cluster hierarchy as a direct means for classification. All training and test instances are first clustered together using an instance-driven pattern-based hierarchical clustering algorithm that allows each instance to "vote" for its representative size-2 patterns in a way that balances local pa...