TY - JOUR
T1 - Perception-based approach to time series data mining
AU - Batyrshin, I. Z.
AU - Sheremetov, L. B.
N1 - Funding Information:
The research work was supported by projects D.00006 and D.00322 of the IMP.
PY - 2008/6
Y1 - 2008/6
N2 - Time series data mining (TSDM) techniques permit exploring large amounts of time series data in search of consistent patterns and/or interesting relationships between variables. TSDM is becoming increasingly important as a knowledge management tool where it is expected to reveal knowledge structures that can guide decision making in conditions of limited certainty. Human decision making in problems related with analysis of time series databases is usually based on perceptions like "end of the day", "high temperature", "quickly increasing", "possible", etc. Though many effective algorithms of TSDM have been developed, the integration of TSDM algorithms with human decision making procedures is still an open problem. In this paper, we consider architecture of perception-based decision making system in time series databases domains integrating perception-based TSDM, computing with words and perceptions, and expert knowledge. The new tasks which should be solved by the perception-based TSDM methods to enable their integration in such systems are discussed. These tasks include: precisiation of perceptions, shape pattern identification, and pattern retranslation. We show how different methods developed so far in TSDM for manipulation of perception-based information can be used for development of a fuzzy perception-based TSDM approach. This approach is grounded in computing with words and perceptions permitting to formalize human perception-based inference mechanisms. The discussion is illustrated by examples from economics, finance, meteorology, medicine, etc.
AB - Time series data mining (TSDM) techniques permit exploring large amounts of time series data in search of consistent patterns and/or interesting relationships between variables. TSDM is becoming increasingly important as a knowledge management tool where it is expected to reveal knowledge structures that can guide decision making in conditions of limited certainty. Human decision making in problems related with analysis of time series databases is usually based on perceptions like "end of the day", "high temperature", "quickly increasing", "possible", etc. Though many effective algorithms of TSDM have been developed, the integration of TSDM algorithms with human decision making procedures is still an open problem. In this paper, we consider architecture of perception-based decision making system in time series databases domains integrating perception-based TSDM, computing with words and perceptions, and expert knowledge. The new tasks which should be solved by the perception-based TSDM methods to enable their integration in such systems are discussed. These tasks include: precisiation of perceptions, shape pattern identification, and pattern retranslation. We show how different methods developed so far in TSDM for manipulation of perception-based information can be used for development of a fuzzy perception-based TSDM approach. This approach is grounded in computing with words and perceptions permitting to formalize human perception-based inference mechanisms. The discussion is illustrated by examples from economics, finance, meteorology, medicine, etc.
KW - Computing with words and perceptions
KW - Data mining
KW - Fuzzy set
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=40649118981&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2007.02.020
DO - 10.1016/j.asoc.2007.02.020
M3 - Artículo
SN - 1568-4946
VL - 8
SP - 1211
EP - 1221
JO - Applied Soft Computing Journal
JF - Applied Soft Computing Journal
IS - 3
ER -