We investigated transform-based algorithms for performing similarity queries in sequential databases. The transform-indexing method of Agrawal, Faloutsos, and Swami (1993) was evaluated, using both Fourier and Walsh transforms to extract features. We extended the method to enable it to handle categorical data as well as different notions of distance. Analytical and experimental evaluation of these methods was performed, using financial data and simulated genetic data
We propose a generic algorithm for computation of similarity measures for se-quential data. The algo...
Abstract: In this paper, a method called MABI (moving average based indexing) is proposed to effect...
We present a fast algorithm for sequence clustering and searching which works with large sequence da...
. We propose an indexing method for time sequences for processing similarity queries. We use the Dis...
We propose an indexing method for time sequences for processing similarity queries. We use the Discr...
Abstract. We propose an indexing method for time sequences for processing similarity queries. We use...
grantor: University of TorontoThe idea of posing queries in terms of similarity of objects...
We study similarity queries for time series data where similarity is defined in terms of a set of li...
We study a set of linear transformations on the Fourier series representation of a sequence that can...
) C. Faloutsos , H. V. Jagadish AT&T Bell Labs Murray Hill, NJ 07974 fchristos,jagg@research...
Efficient and expressive comparison of sequences is an essential procedure for learning with se-quen...
In this paper, we study the problem of sequence similarity search. We incorporate vector transformat...
AbstractWe define the problem of bounded similarity querying in time-series databases, which general...
Problems of analysis and modeling of sequential data arise in many practical applications. In this w...
Computing the similarity between sequences is a very important challenge for many different data min...
We propose a generic algorithm for computation of similarity measures for se-quential data. The algo...
Abstract: In this paper, a method called MABI (moving average based indexing) is proposed to effect...
We present a fast algorithm for sequence clustering and searching which works with large sequence da...
. We propose an indexing method for time sequences for processing similarity queries. We use the Dis...
We propose an indexing method for time sequences for processing similarity queries. We use the Discr...
Abstract. We propose an indexing method for time sequences for processing similarity queries. We use...
grantor: University of TorontoThe idea of posing queries in terms of similarity of objects...
We study similarity queries for time series data where similarity is defined in terms of a set of li...
We study a set of linear transformations on the Fourier series representation of a sequence that can...
) C. Faloutsos , H. V. Jagadish AT&T Bell Labs Murray Hill, NJ 07974 fchristos,jagg@research...
Efficient and expressive comparison of sequences is an essential procedure for learning with se-quen...
In this paper, we study the problem of sequence similarity search. We incorporate vector transformat...
AbstractWe define the problem of bounded similarity querying in time-series databases, which general...
Problems of analysis and modeling of sequential data arise in many practical applications. In this w...
Computing the similarity between sequences is a very important challenge for many different data min...
We propose a generic algorithm for computation of similarity measures for se-quential data. The algo...
Abstract: In this paper, a method called MABI (moving average based indexing) is proposed to effect...
We present a fast algorithm for sequence clustering and searching which works with large sequence da...