Learning distances from categorical attributes is a very useful data mining task that allows to perform distance-based techniques, such as clustering and classification by similarity. In this article we propose a new context-based similarity measure that learns distances between the values of a categorical attribute (DILCA DIstance Learning of Categorical Attributes). We couple our similarity m...

Efficient recognition of haptic data such as 3D motion capture data and sign language sensory data can have wide applications in the interactive computer animation and sign language automatic translation areas. For this purpose, we propose a similarity measure for multi-attribute haptic data, a new form of multimedia signal. The proposed similarity measure, based on singular value decomposition...

In this paper, we offer a geometric framework for the computing of a concept’s conceptual vector based on its similarity position with other concepts in a vector space called concept space, which is a set of concept vectors together with a distance function derived from a similarity model. We show that there exists an isometry to map a concept space to a Euclidean space. So, the concept vector ...

In this paper, we propose an object similarity algorithm (OSA) to address the problem of similarity calculation in the complex graph network by integrating the entity and attribute similarity of the graph node. The proposed method can solve the similarity judgment of the objects that are lack of links or missing attributes by accident or intention. Experimental results have showed that our prop...

We investigate the problem of searching similar multiattribute time sequences. Such sequences arise naturally in a number of medical, financial, video, weather forecast, and stock market databases where more than one attribute is of interest at a time instant. We first solve the simple case in which the distance is defined as the Euclidean distance. Later, we extend it to shift and scale invari...

We present graph-based method of reasoning with if-then rules describing dependencies between attributes in ordinal and similarity-based data. The rules we consider have two basic interpretations as attribute implications in object-attribute incidence data where objects are allowed to have attributes (features) to degrees and as similarity-based functional dependencies in an extension of the Co...

Many modern database applications require content-based similarity search capability in numeric attribute space. Further, users’ notion of similarity varies between search sessions. Therefore online techniques for adaptively refining the similarity metric based on relevance feedback from the user are necessary. Existing methods use retrieved items marked relevant by the user to refine the simil...

Clustering is an important technique for exploratory data analysis. While most of the earlier clustering algorithms focused on numerical data, real-world problems and data mining applications frequently involve categorical data. Here, we propose a new clustering algorithm for categorical data that is based on the frequency of attribute value combinations. Our algorithm finds all the combination...

Similarity concept, finding the resemblance or classifying some groups of objects and study their common properties has been the interest of many researchers. Basically, in the studies the similarity between two objects or phenomena, 2-similarity in our words, has been discussed. In this paper, we consider the case when the resemblance or similarity among three objects or phenomena of a set, 3-...