Mobile location tracking becomes ubiquitous in many applications, which raises great interests in trajectory data analysis and mining. Most existing work tackled the problem of offline trajectory pattern mining. Dynamic discovery and updates of patterns in trajectory data streams in (quasi) real time is a more complex task. In this paper, we propose an incremental algorithm to solve this proble...

We present a new approach to pattern discovery called string pattern regression, where we are given a data set that consists of a string attribute and an objective numerical attribute. The problem is to find the best string pattern that divides the data set in such a way that the distribution of the numerical attribute values of the set for which the pattern matches the string attribute, is mos...

Discovery of frequent patterns is a very important data mining problem with numerous applications. Frequent pattern mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. A significant amount of research on efficient processing of frequent pattern queries has been done in recent years, focusing mainly on co...

Web usage mining refers to the automatic discovery and analysis of patterns in click stream and associated data collected or generated as a result of user interactions with web resources on one or more web sites. It consists of three phases which are data Preprocessing, pattern discovery and pattern analysis. In the pattern discovery phase, frequent pattern discovery algorithms applied on raw d...

Nowadays melanoma is one of the most important cancers to study due to its social impact. This dermatologic cancer has increased its frequency and mortality during last years. In particular, mortality is around twenty percent in non early detected ones. For this reason, the aim of medical researchers is to improve the early diagnosis through a best melanoma characterization using pattern matchi...

Activity discovery is a challenging machine learning problem where we seek to uncover new or altered behavioural patterns in sensor data. In this paper we motivate and introduce a novel approach to activity discovery based on modern deep learning techniques. We hypothesise that our proposed approach can deal with interleaved datasets in a more intelligent manner than most existing AD methods. W...

Information Extraction (IE) systems are commonly based on pattern matching. Adapting an IE system to a new scenario entails the construction of a new pattern base—a timeconsuming and expensive process. We have implemented a system for finding patterns automatically from un-annotated text. Starting with a small initial set of seed patterns proposed by the user, the system applies an incremental ...

Matching of semantic service descriptions is the key to automatic service discovery and binding. Existing approaches split the matchmaking process in two step: signature and specification matching. However, this leads to the problem that offers are not found although they are functionally suitable if their signature is not fitting the requested one. Therefore, in this paper, we propose a matchi...

A new methodology for automated extraction of repeated patterns in time-series data is presented, aimed in particular at the analysis of musical sequences. The basic principles consists in a search for closed patterns in a multi-dimensional parametric space. It is shown that this basic mechanism needs to be articulated with a periodic pattern discovery system, implying therefore a strict chrono...

Automatic extraction of frequent repeated patterns in music material is an interesting problem. This paper presents an effective approach of unsupervised frequent pattern discovery method from symbolic music sources. Patterns are discovered even if they are transposed. Experiments on some songs suggest that our approach is promising, specially when dealing with songs that include non-exact repe...