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Paper Citation Record · LEDGER

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering

As of 19 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2505.02173.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.02173 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T01:06:00.586640Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:16:33.967857Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-15T22:16:34.611774Z

Reference resolution

24 of 24 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9b3aafeb-661c-4581-b072-452f3cbef6cb · outbound

This paper cites James, D.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering James, D

Reference 1

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Observation 03620376-ce99-4001-8871-b251e1737a21 · outbound

This paper cites Impact of selenium nanoparticles on growth, biochemical characteristics and yield of cluster bean cyamopsis tetragonoloba,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Impact of selenium nanoparticles on growth, biochemical characteristics and yield of cluster bean cyamopsis tetragonoloba,

Reference 2

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Observation 4e0aec3a-e8cf-4e08-af41-0639ecb2429c · outbound

This paper cites Fire risk assessment, spatiotemporal clustering and hotspot analysis in the luki biosphere reserve region, western dr congo,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Fire risk assessment, spatiotemporal clustering and hotspot analysis in the luki biosphere reserve region, western dr congo,

Reference 3

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Observation f243d494-5eda-4a11-aea5-1c3af89acae3 · outbound

This paper cites Some methods for classification and analysis of multivariate observations,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Some methods for classification and analysis of multivariate observations,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation ae9c0494-7fce-4f6a-8a41-a5a400f30cba · outbound

This paper cites an unresolved cited work.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Unresolved cited work

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 11daa96e-38d2-4011-a284-1f3886b23263 · outbound

This paper cites A fuzzy relative of the isodata process and its use in detecting compact well- separated clusters,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering A fuzzy relative of the isodata process and its use in detecting compact well- separated clusters,

Reference 6

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Source-reported events for the cited work

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Observation d4f5e184-910e-4fe0-9037-a9efacfc5243 · outbound

This paper cites Maximum likelihood from incomplete data via the em algorithm,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Maximum likelihood from incomplete data via the em algorithm,

Reference 7

Resolution
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Source-reported events for the cited work

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Observation d32233a4-69e3-42dc-b1df-62912075c401 · outbound

This paper cites A density-based algorithm for discovering clusters in large spatial databases with noise,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering A density-based algorithm for discovering clusters in large spatial databases with noise,

Reference 8

Resolution
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Source-reported events for the cited work

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Observation 659449aa-362d-44fd-a7ab-66af1888187b · outbound

This paper cites Dynamic programming algorithm optimization for spoken word recognition,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Dynamic programming algorithm optimization for spoken word recognition,

Reference 9

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6a0e8beb-f030-4a51-a705-0f5c32a4cba2 · outbound

This paper cites Consumer segmentation based on use patterns,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Consumer segmentation based on use patterns,

Reference 10

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 802b8f32-3b58-4742-994b-fabdf2541f18 · outbound

This paper cites Zero-inflated time series clustering via ensemble thick-pen transform,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Zero-inflated time series clustering via ensemble thick-pen transform,

Reference 11

Resolution
verified exact
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Source-reported events for the cited work

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Observation 09b5b595-2e64-49b7-96c8-98b443ec9836 · outbound

This paper cites Co-clustering of time-dependent dat via the shape invariant model,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Co-clustering of time-dependent dat via the shape invariant model,

Reference 12

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 62d20e8a-d879-412d-a584-39dba265f5fc · outbound

This paper cites Correlation-based hierarchical clustering of time series with spatial constraints,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Correlation-based hierarchical clustering of time series with spatial constraints,

Reference 13

Resolution
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Source-reported events for the cited work

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Observation 4c26cb42-fd38-4227-a047-6aac9f96eb8f · outbound

This paper cites Hierarchical clustering of time series data with parametric derivative dynamic time warping,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Hierarchical clustering of time series data with parametric derivative dynamic time warping,

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 049b40ad-9461-483d-b56b-9a21b529a9be · outbound

This paper cites Time-Series K-means in Causal Inference and Mechanism Clustering for Financial Data.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Time-Series K-means in Causal Inference and Mechanism Clustering for Financial Data

Reference 15

Resolution
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Source-reported events for the cited work

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Observation 0ec7231b-0be5-4e48-9edc-afdfc92ae1e6 · outbound

This paper cites On clustering multimedia time series data using k-means and dynamic time warping,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering On clustering multimedia time series data using k-means and dynamic time warping,

Reference 16

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9a049415-f4f2-4901-98cb-e9b29d5b9772 · outbound

This paper cites Solar radiation intensity probabilistic forecasting based on k-means time series clustering and gaussian process regression,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Solar radiation intensity probabilistic forecasting based on k-means time series clustering and gaussian process regression,

Reference 17

Resolution
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Source-reported events for the cited work

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Observation 92f8b8cb-caf4-4685-a280-3c0680689203 · outbound

This paper cites Analysis of electricity consumption pattern clustering and electricity consumption behavior,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Analysis of electricity consumption pattern clustering and electricity consumption behavior,

Reference 18

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation dbe04caf-0e8d-4be4-ba78-f27e310255bb · outbound

This paper cites K-means clustering of electricity consumers using time-domain features from smart meter data,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering K-means clustering of electricity consumers using time-domain features from smart meter data,

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 577345b4-cedd-4d70-b456-b6897a8f5a48 · outbound

This paper cites On Clustering Time Series Using Euclidean Distance and Pearson Correlation.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering On Clustering Time Series Using Euclidean Distance and Pearson Correlation

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2299e4e2-fa22-4a56-8663-30b897847204 · outbound

This paper cites Boehmke and B.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Boehmke and B

Reference 21

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 49732b3d-3bd3-4b07-9406-f22d0e5a2aa2 · outbound

This paper cites Fast global alignment kernels,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Fast global alignment kernels,

Reference 22

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation bf42614d-8a78-4975-bb8b-275186452642 · outbound

This paper cites Clustering performance analysis using a new correlation-based cluster validity index,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering Clustering performance analysis using a new correlation-based cluster validity index,

Reference 23

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 379d51a4-8cfd-4256-8252-201644824dcc · outbound

This paper cites A bayesian cluster validity index,.

Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering A bayesian cluster validity index,

Reference 24

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Pith citing papers

Observation 7dc65824-cf5f-424b-a883-f08933ced908 · inbound

4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients cites this paper.

4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients Ranked differences Pearson correlation dissimilarity with an application to electricity users time series clustering

Reference 34

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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