[PDF] Top 20 Analysis of large data sets using formal concept lattices

... However, let us say that we were only interested in analysing the ‘problem’ data. By only converting these attributes and excluding the demographic ones, a context containing 339,672 concepts was produced; a ... See full document

... ToF-SIMS data analysis, certain systems such as microarrays can still pose a challenge because of the number of separate samples (spots) involved and/or because it is desirable to analyse mm-scale ... See full document

... handle large contexts (Old & Priss, 2004). There is no formal definition of what a large context is, however based on recent benchmarks a large context can be taken as consisting of over ... See full document

... By comparing the contents of each row against each list the words have been cat- egorised as appearing in both or one node. Starting with word sets in common, table 4.5 displays the top 25 frequent words found in ... See full document

... the Formal Concept Analysis formalism in ontology matching ...of formal contexts are constructed in an incremental way, and their derived concept lattices are used to cluster the ... See full document

... between sets of active neurons trans- lates into the corresponding relationship between the encoded ...called Formal Concept Anal- ysis (FCA) [8, 9]. In FCA, data from a binary relation (or ... See full document

... 3. Granularity provides another example. Suppose we have data about objects at two levels of detail or resolution. These might be obtained by some remote- sensing technology. A high-level object can be represented ... See full document

... A standard way of obtaining binary responses from neurons is thresholding the spike count within a certain time window. This is a relatively straightforward task, if the stimuli are presented well separated in time and a ... See full document

... the large and increasing amount of potentially relevant information that is accessible [5, ...and analysis required are not always clear-cut or of a straightfor- ward numerical/statistical nature, but ... See full document

... QBIC [12, 14, 38,46] is the first commercial image retrieval system. It allows a user to pose queries on large image databases based on visual image content, i.e., color texture, etc. Such queries use the visual ... See full document

... enrichment using Logical Concept Analysis ...(Formal Concept Analysis) technique, in which logical formulas are used to represent concepts and generate new ...actual data ... See full document

... between sets of im- ages and features are structured and organized into a hierar- chy, through a formalism called Galois lattices, or concept lat- ... See full document

... in data. FCA is shown to be applicable to large-scale ...the analysis of the Mushroom data set presented here, useful analysis (not presented here) has been carried out on the Adult and ... See full document

... The above model is considered a Saturated Model because it includes all possible one way and two- way effects. The saturated model has the same amount of cells in the contingency table as it does effects as given, the ... See full document

... Dynamic analysis. We present a method called BIA for batch analysis of application's initial permissions and the applying permissions when used in the last ...for analysis of application's applying ... See full document

... clustering using convex optimization. The inputs are two sets: a similarity set and a dis-similarity ...component analysis (NCA) (Goldberger et al., 2004) and large margin nearest neighbor ... See full document

... This project is focusing on the attribute identification of automotive parts. The FAST is used to identify the attributes by (1) the object transform by the process, (2) object produced by the process, (3) performers ... See full document

... rough sets and algebraic systems. The alge- braic approach to rough sets have been given and studied by Bonikowaski in [6], Iwinski in [14], Rosenfeild in [30] and ...fuzzy sets and rough sets ... See full document

... are using a simplistic signal processing method dividing the signal into n parts calculating a standard deviation for each part, which results into the n-dimensional feature vector for each record- ... See full document

... comprises large data set due to the number of variables ...possible data, hoping that no key variables are ...component analysis (PCA) is the most widely ...multivariate data via ... See full document