Data compression, data prediction, data classification, learning and data mining are all strictly related as different points of views, or instances, of the same information treatment problem. Compression inspires information theoretic tools for clustering, pattern discovery and classification. For example it has been recently proposed a new, “ blind ”, approach to clustering by compression that classifies digital objects depending on how they pair - wise compress. We will review this clustering method and we will show how this approach can be used in bio -sequences and medical images clustering
Abstract. We present a novel method for lossless data compression that aims to get a different perfo...
Pattern mining is one of the best-known concepts in Data Mining. A big problem in pattern mining is ...
Density-based clustering algorithms have recently gained popularity in the data mining field due to ...
Data compression, data prediction, data classification, learning and data mining are all strictly re...
Data Compression is today essential for a wide range of applications: for example Internet and the W...
We present a new method for clustering based on compression. The method doesn't use subject-spe...
The need for the ability to cluster unknown data to better understand its relationship to know data ...
The normalized compression distance (NCD) is a similarity measure between a pair of finite objects b...
Data compression, data prediction, data classification, learning and data mining are all facets of t...
International audienceThe use of brain images as markers for diseases or behavioral differences is c...
Abstract—Dealing with data means to group information into a set of categories either in order to le...
Data compression at its base is concerned with how information is organized in data. Understanding t...
Abstract In disease diagnosis, medical image plays an important part. Its lossless compression is pr...
Abstract. We approach the problem of measuring similarity between chromagrams and present two new qu...
During the last decade, image and signal compression for storage and transmission purpose has seen a...
Abstract. We present a novel method for lossless data compression that aims to get a different perfo...
Pattern mining is one of the best-known concepts in Data Mining. A big problem in pattern mining is ...
Density-based clustering algorithms have recently gained popularity in the data mining field due to ...
Data compression, data prediction, data classification, learning and data mining are all strictly re...
Data Compression is today essential for a wide range of applications: for example Internet and the W...
We present a new method for clustering based on compression. The method doesn't use subject-spe...
The need for the ability to cluster unknown data to better understand its relationship to know data ...
The normalized compression distance (NCD) is a similarity measure between a pair of finite objects b...
Data compression, data prediction, data classification, learning and data mining are all facets of t...
International audienceThe use of brain images as markers for diseases or behavioral differences is c...
Abstract—Dealing with data means to group information into a set of categories either in order to le...
Data compression at its base is concerned with how information is organized in data. Understanding t...
Abstract In disease diagnosis, medical image plays an important part. Its lossless compression is pr...
Abstract. We approach the problem of measuring similarity between chromagrams and present two new qu...
During the last decade, image and signal compression for storage and transmission purpose has seen a...
Abstract. We present a novel method for lossless data compression that aims to get a different perfo...
Pattern mining is one of the best-known concepts in Data Mining. A big problem in pattern mining is ...
Density-based clustering algorithms have recently gained popularity in the data mining field due to ...