Abstract—The effectiveness of compression algorithms is increas-ing as the data subjected to compression contains patterns which occur with a certain regularity. This basic idea is used to detect the existence of regularities in various types of data ranging from market basket data to undirected graphs. The results are quite independent of the particular algorithms used for compression and offer an indication of the potential of discovering patterns in data before the actual mining process takes place. Keywords-data mining; lossless compression; LZW; market basket data; patterns; Kronecker product. I
The paper refers to the utility of the algorithm of data compression for purposes other than this. I...
We investigate the use of compression-based learning on graph data. General purpose compressors oper...
Classical rate-distortion theory requires specifying a source distribution. Instead, we analyze rate...
Pattern mining is one of the best-known concepts in Data Mining. A big problem in pattern mining is ...
Data Compression is today essential for a wide range of applications: for example Internet and the W...
This paper studies the behavior of compressed/uncompressed data on predetermined binary patterns. Th...
In data mining it is important for any transforms made to training data to be replicated on evaluat...
The mainstream lossless data compression algorithms have been extensively studied in recent years. H...
Abstract. We present a novel method for lossless data compression that aims to get a different perfo...
Data communication is vital, as the world is getting smaller with the help of Internet. The challeng...
University of Minnesota M.S.E.E. thesis. November 2015. Major: Electrical Engineering. Advisor: Joh...
AiNet Is an Immune-inspired algorithm for data compression, i.e. the reduction of redundancy in data...
The discovery of patterns plays an important role in data mining. A pattern can be any type of regul...
Data compression algorithms are widely employed to reduce the amount of data in order to save storag...
University of Minnesota M.S. thesis. June 2018. Major: Computer Science. Advisor: Peter Peterson. 1 ...
The paper refers to the utility of the algorithm of data compression for purposes other than this. I...
We investigate the use of compression-based learning on graph data. General purpose compressors oper...
Classical rate-distortion theory requires specifying a source distribution. Instead, we analyze rate...
Pattern mining is one of the best-known concepts in Data Mining. A big problem in pattern mining is ...
Data Compression is today essential for a wide range of applications: for example Internet and the W...
This paper studies the behavior of compressed/uncompressed data on predetermined binary patterns. Th...
In data mining it is important for any transforms made to training data to be replicated on evaluat...
The mainstream lossless data compression algorithms have been extensively studied in recent years. H...
Abstract. We present a novel method for lossless data compression that aims to get a different perfo...
Data communication is vital, as the world is getting smaller with the help of Internet. The challeng...
University of Minnesota M.S.E.E. thesis. November 2015. Major: Electrical Engineering. Advisor: Joh...
AiNet Is an Immune-inspired algorithm for data compression, i.e. the reduction of redundancy in data...
The discovery of patterns plays an important role in data mining. A pattern can be any type of regul...
Data compression algorithms are widely employed to reduce the amount of data in order to save storag...
University of Minnesota M.S. thesis. June 2018. Major: Computer Science. Advisor: Peter Peterson. 1 ...
The paper refers to the utility of the algorithm of data compression for purposes other than this. I...
We investigate the use of compression-based learning on graph data. General purpose compressors oper...
Classical rate-distortion theory requires specifying a source distribution. Instead, we analyze rate...